Coral Physiology Following Reciprocal Transplant
Serena Hackerott
1/1/2024
Setup
Load Packages
##Install Packages if Needed
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("cowplot")) install.packages("cowplot")
if (!require("Rmisc")) install.packages("Rmisc")
if (!require("lme4")) install.packages("lme4")Loading required package: lme4
Loading required package: Matrixif (!require("vegan")) install.packages("vegan")
if (!require("corrplot")) install.packages("corrplot")
if (!require("DHARMa")) install.packages("DHARMa")Loading required package: DHARMa
This is DHARMa 0.4.6. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')if (!require("effectsize")) install.packages("effectsize")Loading required package: effectsizeif (!require("emmeans")) install.packages("emmeans")Loading required package: emmeans##Load Packages
library(ggplot2) #Required for ggplots
library(cowplot) #Required for plotting panel figures
library(Rmisc) #Required for SummarySE function
library(lme4) #Required for mixed effects modeling
library(vegan) #Required for multivariate analysis PERM
library(corrplot) #Required for correlation plot
#library(lmerTest)
library(DHARMa) #Required to check residuals of mixed effects models
library(effectsize) #Required for eta_squared effect sizes
library(emmeans) #Required for pairwise comparisons
Graphing Parameters
#Note: Run "Graphing Parameters" section from 01_ExperimentalSetup.Rmd fileSample Data and Metadata
Load and Organize Data
##Load Data
#Note: Physiological metrics calculated in 02_PhysiologyMetrics.R file
Coral<-read.csv("Outputs/CoralData.csv", header=TRUE)
##Set factor variables
Coral$TimeP<-factor(Coral$TimeP, levels=c("TP1", "TP2", "TP3", "TP4"))
Coral$Site<-factor(Coral$Site, levels=c("KL", "SS"))
Coral$Genotype<-factor(Coral$Genotype, levels=c("AC8", "AC10", "AC12"))
Coral$Orig<-factor(Coral$Orig, levels=c("N", "T"))
Coral$Origin<-factor(Coral$Origin, levels=c("Native", "Transplant"))
Coral$Site.Orig<-factor(Coral$Site.Orig, levels=c("KL.N", "KL.T", "SS.N", "SS.T"))
Multivariate Analysis
adonis OmegaSq Function
#' Calculate (partial) Omega-squared (effect-size calculation) for PERMANOVA and add it to the input object
#'
#' @param adonisOutput An adonis object
#' @param partial Should partial omega-squared be calculated (sample size adjusted). Default TRUE
#' @return Original adonis object with the (partial) Omega-squared values added
#' @import vegan
#' @export
adonis_OmegaSq <- function(adonisOutput, partial = TRUE){
if(!(is(adonisOutput, "adonis") || is(adonisOutput, "anova.cca")))
stop("Input should be an adonis object")
if (is(adonisOutput, "anova.cca")) {
aov_tab <- adonisOutput
aov_tab$MeanSqs <- aov_tab$SumOfSqs / aov_tab$Df
aov_tab$MeanSqs[length(aov_tab$Df)] <- NA
} else {
aov_tab <- adonisOutput$aov.tab
}
heading <- attr(aov_tab, "heading")
MS_res <- aov_tab[pmatch("Residual", rownames(aov_tab)), "MeanSqs"]
SS_tot <- aov_tab[rownames(aov_tab) == "Total", "SumsOfSqs"]
N <- aov_tab[rownames(aov_tab) == "Total", "Df"] + 1
if(partial){
omega <- apply(aov_tab, 1, function(x) (x["Df"]*(x["MeanSqs"]-MS_res))/(x["Df"]*x["MeanSqs"]+(N-x["Df"])*MS_res))
aov_tab$parOmegaSq <- c(omega[1:(length(omega)-2)], NA, NA)
} else {
omega <- apply(aov_tab, 1, function(x) (x["SumsOfSqs"]-x["Df"]*MS_res)/(SS_tot+MS_res))
aov_tab$OmegaSq <- c(omega[1:(length(omega)-2)], NA, NA)
}
if (is(adonisOutput, "adonis"))
cn_order <- c("Df", "SumsOfSqs", "MeanSqs", "F.Model", "R2",
if (partial) "parOmegaSq" else "OmegaSq", "Pr(>F)")
else
cn_order <- c("Df", "SumOfSqs", "F", if (partial) "parOmegaSq" else "OmegaSq",
"Pr(>F)")
aov_tab <- aov_tab[, cn_order]
attr(aov_tab, "names") <- cn_order
attr(aov_tab, "heading") <- heading
if (is(adonisOutput, "adonis"))
adonisOutput$aov.tab <- aov_tab
else
adonisOutput <- aov_tab
return(adonisOutput)
}Check Correlation
##Remove NA's
names(Coral) [1] "ID" "RandN" "TimeP" "Site"
[5] "Genotype" "Orig" "Origin" "Set"
[9] "Site.Orig" "SA_cm2" "TP_ug.cm2_C" "TP_ug.cm2_S"
[13] "AFDW_mg.cm2_C" "AFDW_mg.cm2_S" "AFDW_mg.cm2_S.C" "Chl_ug.cm2" Coral.rm<-na.omit(Coral)
##Log +1 transform
Coral.log<-Coral.rm
Coral.log[,-c(1:10)]<-log(Coral.rm[,-c(1:10)]+1)
Phys.cor<-rcorr(as.matrix(Coral.log[,-c(1:10)]), type="pearson")
Phys.cor TP_ug.cm2_C TP_ug.cm2_S AFDW_mg.cm2_C AFDW_mg.cm2_S AFDW_mg.cm2_S.C
TP_ug.cm2_C 1.00 0.56 0.55 0.48 0.03
TP_ug.cm2_S 0.56 1.00 0.37 0.41 0.08
AFDW_mg.cm2_C 0.55 0.37 1.00 0.37 -0.49
AFDW_mg.cm2_S 0.48 0.41 0.37 1.00 0.60
AFDW_mg.cm2_S.C 0.03 0.08 -0.49 0.60 1.00
Chl_ug.cm2 0.37 0.16 0.33 0.50 0.20
Chl_ug.cm2
TP_ug.cm2_C 0.37
TP_ug.cm2_S 0.16
AFDW_mg.cm2_C 0.33
AFDW_mg.cm2_S 0.50
AFDW_mg.cm2_S.C 0.20
Chl_ug.cm2 1.00
n= 190
P
TP_ug.cm2_C TP_ug.cm2_S AFDW_mg.cm2_C AFDW_mg.cm2_S AFDW_mg.cm2_S.C
TP_ug.cm2_C 0.0000 0.0000 0.0000 0.6401
TP_ug.cm2_S 0.0000 0.0000 0.0000 0.2762
AFDW_mg.cm2_C 0.0000 0.0000 0.0000 0.0000
AFDW_mg.cm2_S 0.0000 0.0000 0.0000 0.0000
AFDW_mg.cm2_S.C 0.6401 0.2762 0.0000 0.0000
Chl_ug.cm2 0.0000 0.0248 0.0000 0.0000 0.0049
Chl_ug.cm2
TP_ug.cm2_C 0.0000
TP_ug.cm2_S 0.0248
AFDW_mg.cm2_C 0.0000
AFDW_mg.cm2_S 0.0000
AFDW_mg.cm2_S.C 0.0049
Chl_ug.cm2 diag(Phys.cor$P)<-0
corrplot(Phys.cor$r, type="upper", order="hclust",
p.mat = Phys.cor$P, sig.level = 0.01, insig = "blank")
TP1
##Subset Timepoint 1
Coral.log_TP1<-subset(Coral.log, TimeP=="TP1")
PERMANOVA
##PERMANOVA
Coral.TP1.perm<-adonis2(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin * Coral.log_TP1$Site, data=Coral.log_TP1, strata=Coral.log_TP1$Genotype, method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP1.perm)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP1[, c(11:14, 16)], "euclidean") ~ Coral.log_TP1$Origin * Coral.log_TP1$Site, data = Coral.log_TP1, method = "euclidean", strata = Coral.log_TP1$Genotype)
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP1$Origin 1 0.2789 2.7121 0.034440 0.012 *
Coral.log_TP1$Site 1 0.3722 3.6196 0.051750 0.003 **
Coral.log_TP1$Origin:Coral.log_TP1$Site 1 0.3433 3.3384 0.046454 0.004 **
Residual 44 4.5240
Total 47 5.5183
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.01547 0.015468 1.0199 0.3178
Residuals 46 0.69768 0.015167 ##Check dispersion by Site
anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Site))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.04842 0.048424 3.1669 0.08175 .
Residuals 46 0.70338 0.015291
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin:Site
anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin:Coral.log_TP1$Site))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 3 0.02212 0.0073726 0.5649 0.641
Residuals 44 0.57425 0.0130512 Physiology differs significantly by Origin and the effect of Origin differs between Sites.
Effect Size
Calculate Effect Size of Origin for each Site
##KL
##PERMANOVA
Coral.TP1.perm_KL<-adonis2(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="KL")], data=Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),], strata=Coral.log_TP1$Genotype[which(Coral.log_TP1$Site=="KL")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP1.perm_KL)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP1[which(Coral.log_TP1$Site == "KL"), c(11:14, 16)], "euclidean") ~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "KL")], data = Coral.log_TP1[which(Coral.log_TP1$Site == "KL"), ], method = "euclidean", strata = Coral.log_TP1$Genotype[which(Coral.log_TP1$Site == "KL")])
Df SumOfSqs F parOmegaSq
Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "KL")] 1 0.05913 0.6453 -0.015001
Residual 22 2.01586
Total 23 2.07498
Pr(>F)
Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "KL")] 0.437
Residual
Total ##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="KL")]))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.002019 0.0020194 0.1424 0.7095
Residuals 22 0.311986 0.0141812 ##SS
##PERMANOVA
Coral.TP1.perm_SS<-adonis2(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="SS")], data=Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),], strata=Coral.log_TP1$Genotype[which(Coral.log_TP1$Site=="SS")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP1.perm_SS)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP1[which(Coral.log_TP1$Site == "SS"), c(11:14, 16)], "euclidean") ~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "SS")], data = Coral.log_TP1[which(Coral.log_TP1$Site == "SS"), ], method = "euclidean", strata = Coral.log_TP1$Genotype[which(Coral.log_TP1$Site == "SS")])
Df SumOfSqs F parOmegaSq
Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "SS")] 1 0.56298 4.9381 0.14096
Residual 22 2.50815
Total 23 3.07113
Pr(>F)
Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "SS")] 0.001 ***
Residual
Total
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="SS")]))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.000018 0.0000178 0.0015 0.9695
Residuals 22 0.262265 0.0119211 Variance Partitioning
##Variance Partitioning
Coral.TP1.vp<-varpart(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Origin, Coral.log_TP1$Site, Coral.log_TP1$Genotype)
Coral.TP1.vp$partNo. of explanatory tables: 3
Total variation (SS): 5.5183
Variance: 0.11741
No. of observations: 48
Partition table:---
Use function ‘rda’ to test significance of fractions of interest##Check model significance
anova(rda(Coral.log_TP1[,c(11:14, 16)]~ Coral.log_TP1$Origin + Coral.log_TP1$Site + Coral.log_TP1$Genotype))Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999
Model: rda(formula = Coral.log_TP1[, c(11:14, 16)] ~ Coral.log_TP1$Origin + Coral.log_TP1$Site + Coral.log_TP1$Genotype)
Df Variance F Pr(>F)
Model 4 0.047741 7.3665 0.001 ***
Residual 43 0.069669
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check variance explained by model
RsquareAdj(rda(Coral.log_TP1[,c(11:14, 16)]~ Coral.log_TP1$Origin + Coral.log_TP1$Site + Coral.log_TP1$Genotype))$adj.r.squared*100[1] 35.1419##Check effect of Origin
anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Origin))Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999
Model: rda(X = Coral.log_TP1[, c(11:14, 16)], Y = Coral.log_TP1$Origin)
Df Variance F Pr(>F)
Model 1 0.005933 2.4482 0.068 .
Residual 46 0.111477
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Origin, Coral.log_TP1[,c(4:5)]))Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999
Model: rda(X = Coral.log_TP1[, c(11:14, 16)], Y = Coral.log_TP1$Origin, Z = Coral.log_TP1[, c(4:5)])
Df Variance F Pr(>F)
Model 1 0.005933 3.6619 0.014 *
Residual 43 0.069669
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check effect of Site
anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Site))Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999
Model: rda(X = Coral.log_TP1[, c(11:14, 16)], Y = Coral.log_TP1$Site)
Df Variance F Pr(>F)
Model 1 0.007918 3.3266 0.021 *
Residual 46 0.109492
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Site, Coral.log_TP1[,c(5,7)]))Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999
Model: rda(X = Coral.log_TP1[, c(11:14, 16)], Y = Coral.log_TP1$Site, Z = Coral.log_TP1[, c(5, 7)])
Df Variance F Pr(>F)
Model 1 0.007918 4.8872 0.005 **
Residual 43 0.069669
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check effect of Genotype
anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Genotype))Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999
Model: rda(X = Coral.log_TP1[, c(11:14, 16)], Y = Coral.log_TP1$Genotype)
Df Variance F Pr(>F)
Model 2 0.03389 9.1297 0.001 ***
Residual 45 0.08352
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Genotype, Coral.log_TP1[,c(4,7)]))Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999
Model: rda(X = Coral.log_TP1[, c(11:14, 16)], Y = Coral.log_TP1$Genotype, Z = Coral.log_TP1[, c(4, 7)])
Df Variance F Pr(>F)
Model 2 0.033890 10.458 0.001 ***
Residual 43 0.069669
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1The model is significant (p = 0.001) and explains 35.14% of the variance of physiology. The effect of Origin (controlling for Site and Genotype) is significant (p = 0.013) and explains 3.92%. The effect of Site (controlling for Origin and Genotype) is significant (p = 0.001) and explains 5.73%. The effect of Genotype (controlling for Origin and Site) is significant (p = 0.001) and explains 27.27%.
TP2
##Subset Timepoint 2
Coral.log_TP2<-subset(Coral.log, TimeP=="TP2")
PERMANOVA
##PERMANOVA
Coral.TP2.perm<-adonis2(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin * Coral.log_TP2$Site, data=Coral.log_TP2, strata=Coral.log_TP2$Genotype, method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP2.perm)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP2[, c(11:14, 16)], "euclidean") ~ Coral.log_TP2$Origin * Coral.log_TP2$Site, data = Coral.log_TP2, method = "euclidean", strata = Coral.log_TP2$Genotype)
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP2$Origin 1 0.0347 0.420 -0.01223 0.660
Coral.log_TP2$Site 1 2.0831 25.239 0.33554 0.001 ***
Coral.log_TP2$Origin:Coral.log_TP2$Site 1 0.2762 3.346 0.04660 0.050 *
Residual 44 3.6315
Total 47 6.0255
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.02892 0.028918 1.1826 0.2825
Residuals 46 1.12482 0.024453 ##Check dispersion by Site
anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Site))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00701 0.0070126 0.5917 0.4457
Residuals 46 0.54518 0.0118518 ##Check dispersion by Origin:Site
anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin:Coral.log_TP2$Site))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 3 0.02003 0.0066763 0.6202 0.6057
Residuals 44 0.47364 0.0107645 Effect Size
Calculate Effect Size of Origin for each Site
##KL
##PERMANOVA
Coral.TP2.perm_KL<-adonis2(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="KL")], data=Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),], strata=Coral.log_TP2$Genotype[which(Coral.log_TP2$Site=="KL")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP2.perm_KL)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP2[which(Coral.log_TP2$Site == "KL"), c(11:14, 16)], "euclidean") ~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "KL")], data = Coral.log_TP2[which(Coral.log_TP2$Site == "KL"), ], method = "euclidean", strata = Coral.log_TP2$Genotype[which(Coral.log_TP2$Site == "KL")])
Df SumOfSqs F parOmegaSq
Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "KL")] 1 0.18146 2.5268 0.059813
Residual 22 1.57991
Total 23 1.76137
Pr(>F)
Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "KL")] 0.045 *
Residual
Total
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="KL")]))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.000732 0.0007320 0.0896 0.7675
Residuals 22 0.179698 0.0081681 ##SS
##PERMANOVA
Coral.TP2.perm_SS<-adonis2(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="SS")], data=Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),], strata=Coral.log_TP2$Genotype[which(Coral.log_TP2$Site=="SS")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP2.perm_SS)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP2[which(Coral.log_TP2$Site == "SS"), c(11:14, 16)], "euclidean") ~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "SS")], data = Coral.log_TP2[which(Coral.log_TP2$Site == "SS"), ], method = "euclidean", strata = Coral.log_TP2$Genotype[which(Coral.log_TP2$Site == "SS")])
Df SumOfSqs F parOmegaSq
Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "SS")] 1 0.12937 1.3872 0.015878
Residual 22 2.05163
Total 23 2.18100
Pr(>F)
Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "SS")] 0.166
Residual
Total ##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="SS")]))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.006276 0.0062764 0.4698 0.5003
Residuals 22 0.293942 0.0133610 TP3
##Subset Timepoint 3
Coral.log_TP3<-subset(Coral.log, TimeP=="TP3")
PERMANOVA
##PERMANOVA
Coral.TP3.perm<-adonis2(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin * Coral.log_TP3$Site, data=Coral.log_TP3, strata=Coral.log_TP3$Genotype, method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP3.perm)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP3[, c(11:14, 16)], "euclidean") ~ Coral.log_TP3$Origin * Coral.log_TP3$Site, data = Coral.log_TP3, method = "euclidean", strata = Coral.log_TP3$Genotype)
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP3$Origin 1 0.1051 0.7853 -0.004493 0.361
Coral.log_TP3$Site 1 1.4021 10.4738 0.164837 0.001 ***
Coral.log_TP3$Origin:Coral.log_TP3$Site 1 0.1024 0.7649 -0.004921 0.378
Residual 44 5.8901
Total 47 7.4997
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.06528 0.065278 2.0695 0.157
Residuals 46 1.45099 0.031543 ##Check dispersion by Site
anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Site))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00202 0.002024 0.0609 0.8062
Residuals 46 1.52934 0.033246 ##Check dispersion by Origin:Site
anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin:Coral.log_TP3$Site))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 3 0.09225 0.030751 0.975 0.4131
Residuals 44 1.38769 0.031538 Effect Size
Calculate Effect Size of Origin for each Site
##KL
##PERMANOVA
Coral.TP3.perm_KL<-adonis2(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="KL")], data=Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),], strata=Coral.log_TP3$Genotype[which(Coral.log_TP3$Site=="KL")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP3.perm_KL)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP3[which(Coral.log_TP3$Site == "KL"), c(11:14, 16)], "euclidean") ~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "KL")], data = Coral.log_TP3[which(Coral.log_TP3$Site == "KL"), ], method = "euclidean", strata = Coral.log_TP3$Genotype[which(Coral.log_TP3$Site == "KL")])
Df SumOfSqs F parOmegaSq
Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "KL")] 1 0.17515 1.5145 0.020987
Residual 22 2.54431
Total 23 2.71946
Pr(>F)
Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "KL")] 0.153
Residual
Total ##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="KL")]))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00703 0.0070337 0.3193 0.5778
Residuals 22 0.48467 0.0220307 ##SS
##PERMANOVA
Coral.TP3.perm_SS<-adonis2(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="SS")], data=Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),], strata=Coral.log_TP3$Genotype[which(Coral.log_TP3$Site=="SS")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP3.perm_SS)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP3[which(Coral.log_TP3$Site == "SS"), c(11:14, 16)], "euclidean") ~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "SS")], data = Coral.log_TP3[which(Coral.log_TP3$Site == "SS"), ], method = "euclidean", strata = Coral.log_TP3$Genotype[which(Coral.log_TP3$Site == "SS")])
Df SumOfSqs F parOmegaSq
Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "SS")] 1 0.0324 0.2129 -0.03391
Residual 22 3.3458
Total 23 3.3782
Pr(>F)
Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "SS")] 0.772
Residual
Total ##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="SS")]))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.07949 0.079485 1.9365 0.178
Residuals 22 0.90301 0.041046 TP4
##Subset Timepoint 4
Coral.log_TP4<-subset(Coral.log, TimeP=="TP4")
PERMANOVA
##PERMANOVA
Coral.TP4.perm<-adonis2(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin * Coral.log_TP4$Site, data=Coral.log_TP4, strata=Coral.log_TP4$Genotype, method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP4.perm)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP4[, c(11:14, 16)], "euclidean") ~ Coral.log_TP4$Origin * Coral.log_TP4$Site, data = Coral.log_TP4, method = "euclidean", strata = Coral.log_TP4$Genotype)
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP4$Origin 1 0.0204 0.1301 -0.019276 0.921
Coral.log_TP4$Site 1 2.5446 16.2273 0.248701 0.001 ***
Coral.log_TP4$Origin:Coral.log_TP4$Site 1 0.2287 1.4586 0.009870 0.214
Residual 42 6.5861
Total 45 9.3799
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00639 0.006388 0.1952 0.6608
Residuals 44 1.44022 0.032732 ##Check dispersion by Site
anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Site))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.01153 0.011529 0.3704 0.5459
Residuals 44 1.36932 0.031121 ##Check dispersion by Origin:Site
anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin:Coral.log_TP4$Site))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 3 0.04203 0.014012 0.4161 0.7423
Residuals 42 1.41428 0.033673 Effect Size
Calculate Effect Size of Origin for each Site
##KL
##PERMANOVA
Coral.TP4.perm_KL<-adonis2(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="KL")], data=Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),], strata=Coral.log_TP4$Genotype[which(Coral.log_TP4$Site=="KL")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP4.perm_KL)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP4[which(Coral.log_TP4$Site == "KL"), c(11:14, 16)], "euclidean") ~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site == "KL")], data = Coral.log_TP4[which(Coral.log_TP4$Site == "KL"), ], method = "euclidean", strata = Coral.log_TP4$Genotype[which(Coral.log_TP4$Site == "KL")])
Df SumOfSqs F parOmegaSq
Coral.log_TP4$Origin[which(Coral.log_TP4$Site == "KL")] 1 0.0676 0.3774 -0.027823
Residual 21 3.7601
Total 22 3.8277
Pr(>F)
Coral.log_TP4$Origin[which(Coral.log_TP4$Site == "KL")] 0.676
Residual
Total ##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="KL")]))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00316 0.003160 0.0776 0.7833
Residuals 21 0.85503 0.040716 ##SS
##PERMANOVA
Coral.TP4.perm_SS<-adonis2(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="SS")], data=Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),], strata=Coral.log_TP4$Genotype[which(Coral.log_TP4$Site=="SS")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP4.perm_SS)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP4[which(Coral.log_TP4$Site == "SS"), c(11:14, 16)], "euclidean") ~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site == "SS")], data = Coral.log_TP4[which(Coral.log_TP4$Site == "SS"), ], method = "euclidean", strata = Coral.log_TP4$Genotype[which(Coral.log_TP4$Site == "SS")])
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP4$Origin[which(Coral.log_TP4$Site == "SS")] 1 0.18154 1.349 0.014948 0.149
Residual 21 2.82599
Total 22 3.00753 ##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="SS")]))Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.02157 0.021572 0.81 0.3783
Residuals 21 0.55925 0.026631 Univariate
TP1
##Subset Timepoint 1
Coral.TP1<-subset(Coral.rm, TimeP=="TP1")
Protein Host
##Check normality
hist(Coral.TP1$TP_ug.cm2_C)
shapiro.test(Coral.TP1$TP_ug.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP1$TP_ug.cm2_C
W = 0.98637, p-value = 0.8448#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP1<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Prot.C.lme_res_TP1 <- simulateResiduals(fittedModel = Prot.C.lme_TP1, plot = F)
plot(Prot.C.lme_res_TP1)
##Model results
summary(Prot.C.lme_TP1)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: 476.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.98711 -0.58176 -0.00143 0.76130 1.61635
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 1703 41.27
Residual 2107 45.91
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 340.714 27.262 12.498
OriginTransplant 3.303 18.741 0.176
SiteSS 55.261 18.741 2.949
OriginTransplant:SiteSS -31.334 26.504 -1.182
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.344
SiteSS -0.344 0.500
OrgnTrn:SSS 0.243 -0.707 -0.707eta_squared(Prot.C.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.02 | [0.00, 1.00]
Site | 0.18 | [0.04, 1.00]
Origin:Site | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Protein Symbiont
##Check normality
hist(Coral.TP1$TP_ug.cm2_S)
shapiro.test(Coral.TP1$TP_ug.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP1$TP_ug.cm2_S
W = 0.96018, p-value = 0.1027#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP1<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Prot.S.lme_res_TP1 <- simulateResiduals(fittedModel = Prot.S.lme_TP1, plot = F)
plot(Prot.S.lme_res_TP1)
##Model results
summary(Prot.S.lme_TP1)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: 504.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.99771 -0.58981 -0.06134 0.48297 2.49136
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 1613 40.17
Residual 4087 63.93
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 459.014 29.638 15.488
OriginTransplant 1.258 26.100 0.048
SiteSS 42.145 26.100 1.615
OriginTransplant:SiteSS -86.531 36.910 -2.344
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.440
SiteSS -0.440 0.500
OrgnTrn:SSS 0.311 -0.707 -0.707eta_squared(Prot.S.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.11 | [0.01, 1.00]
Site | 8.78e-05 | [0.00, 1.00]
Origin:Site | 0.12 | [0.01, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Prot.S.lme_TP1_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Prot.S.lme_TP1_KL)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "KL"), ]
REML criterion at convergence: 254.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.64984 -0.68376 -0.07079 0.30913 2.31492
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 2602 51.01
Residual 4223 64.99
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 459.014 34.918 13.145
OriginTransplant 1.258 26.531 0.047
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.380eta_squared(Prot.S.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 1.12e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Prot.S.lme_TP1_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Prot.S.lme_TP1_SS)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "SS"), ]
REML criterion at convergence: 251.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.7405 -0.5819 -0.2580 0.6750 2.1154
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 695.5 26.37
Residual 3899.6 62.45
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 501.16 23.60 21.239
OriginTransplant -85.27 25.49 -3.345
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.540eta_squared(Prot.S.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.36 | [0.10, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP1_ProtSym.sum<-summarySE(Coral.TP1, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Protein across Treatments
TP1_ProtSym.plot<-ggplot(TP1_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(350, 550)+
annotate("text", x=2, y=535, label="**", size=sig.sz, fontface="bold"); TP1_ProtSym.plot
Biomass Host
##Check normality
hist(Coral.TP1$AFDW_mg.cm2_C)
shapiro.test(Coral.TP1$AFDW_mg.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP1$AFDW_mg.cm2_C
W = 0.96547, p-value = 0.1677#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP1<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Bio.C.lme_res_TP1 <- simulateResiduals(fittedModel = Bio.C.lme_TP1, plot = F)
plot(Bio.C.lme_res_TP1)
##Model results
summary(Bio.C.lme_TP1)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: 17.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.3616 -0.6323 0.1012 0.6827 1.4522
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.006352 0.0797
Residual 0.067274 0.2594
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.20320 0.08788 13.691
OriginTransplant -0.18538 0.10589 -1.751
SiteSS -0.01392 0.10589 -0.131
OriginTransplant:SiteSS 0.04772 0.14975 0.319
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.602
SiteSS -0.602 0.500
OrgnTrn:SSS 0.426 -0.707 -0.707eta_squared(Bio.C.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.10 | [0.00, 1.00]
Site | 4.20e-04 | [0.00, 1.00]
Origin:Site | 2.41e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Bio.C.lme_TP1_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Bio.C.lme_TP1_KL)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "KL"), ]
REML criterion at convergence: 1.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.61169 -0.43907 0.07185 0.60072 1.71208
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.003322 0.05764
Residual 0.047206 0.21727
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.2032 0.0710 16.95
OriginTransplant -0.1854 0.0887 -2.09
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.625eta_squared(Bio.C.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.18 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.C.lme_TP1_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Bio.C.lme_TP1_SS)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "SS"), ]
REML criterion at convergence: 15.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.9881 -0.5903 0.3895 0.6868 1.3845
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0002328 0.01526
Residual 0.0939949 0.30659
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.18928 0.08894 13.37
OriginTransplant -0.13767 0.12516 -1.10
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.704eta_squared(Bio.C.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.06 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP1_BioHost.sum<-summarySE(Coral.TP1, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Host Biomass across Treatments
TP1_BioHost.plot<-ggplot(TP1_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0, 2)+
annotate("text", x=1, y=1.5, label="*", size=sig.sz, fontface="bold"); TP1_BioHost.plot
Biomass Symbionts
##Check normality
hist(Coral.TP1$AFDW_mg.cm2_S)
shapiro.test(Coral.TP1$AFDW_mg.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP1$AFDW_mg.cm2_S
W = 0.97584, p-value = 0.4194#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP1<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Bio.S.lme_res_TP1 <- simulateResiduals(fittedModel = Bio.S.lme_TP1, plot = F)
plot(Bio.S.lme_res_TP1)
##Model results
summary(Bio.S.lme_TP1)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: -29.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.71364 -0.74817 -0.06341 0.60227 2.13813
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002785 0.05277
Residual 0.022713 0.15071
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.57323 0.05311 10.792
OriginTransplant 0.01058 0.06153 0.172
SiteSS 0.16334 0.06153 2.655
OriginTransplant:SiteSS -0.22488 0.08701 -2.584
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.579
SiteSS -0.579 0.500
OrgnTrn:SSS 0.410 -0.707 -0.707eta_squared(Bio.S.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.12 | [0.01, 1.00]
Site | 0.03 | [0.00, 1.00]
Origin:Site | 0.14 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Bio.S.lme_TP1_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Bio.S.lme_TP1_KL)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "KL"), ]
REML criterion at convergence: -21.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.72868 -0.71431 0.04303 0.52737 1.90341
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.01040 0.1020
Residual 0.01508 0.1228
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.57323 0.06873 8.340
OriginTransplant 0.01058 0.05014 0.211
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.365eta_squared(Bio.S.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 2.22e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.S.lme_TP1_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Bio.S.lme_TP1_SS)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "SS"), ]
REML criterion at convergence: -13.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.2456 -0.7860 -0.1378 0.6402 2.1806
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.004289 0.06549
Residual 0.023707 0.15397
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.73657 0.05836 12.622
OriginTransplant -0.21429 0.06286 -3.409
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.539eta_squared(Bio.S.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.37 | [0.10, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP1_BioSym.sum<-summarySE(Coral.TP1, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Biomass across Treatments
TP1_BioSym.plot<-ggplot(TP1_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0, 1)+
annotate("text", x=2, y=0.85, label="**", size=sig.sz, fontface="bold"); TP1_BioSym.plot
Chlorophyll
##Check normality
hist(Coral.TP1$Chl_ug.cm2)
shapiro.test(Coral.TP1$Chl_ug.cm2)
Shapiro-Wilk normality test
data: Coral.TP1$Chl_ug.cm2
W = 0.95564, p-value = 0.06727#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP1<-lmer(Chl_ug.cm2~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Chl.lme_res_TP1 <- simulateResiduals(fittedModel = Chl.lme_TP1, plot = F)
plot(Chl.lme_res_TP1)
##Model results
summary(Chl.lme_TP1)Linear mixed model fit by REML ['lmerMod']
Formula: Chl_ug.cm2 ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: -0.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.1377 -0.5871 -0.1548 0.6976 2.0162
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.08669 0.2944
Residual 0.03936 0.1984
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.86074 0.17937 4.799
OriginTransplant 0.07568 0.08099 0.934
SiteSS 0.54382 0.08099 6.715
OriginTransplant:SiteSS -0.48115 0.11454 -4.201
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.226
SiteSS -0.226 0.500
OrgnTrn:SSS 0.160 -0.707 -0.707eta_squared(Chl.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.16 | [0.03, 1.00]
Site | 0.40 | [0.21, 1.00]
Origin:Site | 0.30 | [0.12, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Chl.lme_TP1_KL<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Chl.lme_TP1_KL)Linear mixed model fit by REML ['lmerMod']
Formula: Chl_ug.cm2 ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "KL"), ]
REML criterion at convergence: -4.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.42282 -0.68856 0.00126 0.53926 2.32168
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.05037 0.2244
Residual 0.02931 0.1712
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.86074 0.13868 6.207
OriginTransplant 0.07568 0.06989 1.083
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.252eta_squared(Chl.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.06 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Chl.lme_TP1_SS<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Chl.lme_TP1_SS)Linear mixed model fit by REML ['lmerMod']
Formula: Chl_ug.cm2 ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "SS"), ]
REML criterion at convergence: 2.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.0949 -0.5799 0.1160 0.6297 1.7750
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.13767 0.3710
Residual 0.03874 0.1968
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.40456 0.22163 6.338
OriginTransplant -0.40548 0.08035 -5.046
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.181eta_squared(Chl.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.56 | [0.30, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP1_Chl.sum<-summarySE(Coral.TP1, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Chlorophyll across Treatments
TP1_Chl.plot<-ggplot(TP1_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(0, 2)+
annotate("text", x=2, y=1.7, label="***", size=sig.sz, fontface="bold"); TP1_Chl.plot
TP2
##Subset Timepoint 2
Coral.TP2<-subset(Coral.rm, TimeP=="TP2")
Protein Host
##Check normality
hist(Coral.TP2$TP_ug.cm2_C)
shapiro.test(Coral.TP2$TP_ug.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP2$TP_ug.cm2_C
W = 0.9796, p-value = 0.562#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP2<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Prot.C.lme_res_TP2 <- simulateResiduals(fittedModel = Prot.C.lme_TP2, plot = F)
plot(Prot.C.lme_res_TP2)
##Model results
summary(Prot.C.lme_TP2)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: 469.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.67310 -0.67673 -0.00324 0.57592 1.67096
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 391.9 19.80
Residual 1877.0 43.32
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 315.853 16.942 18.643
OriginTransplant -1.965 17.687 -0.111
SiteSS 42.263 17.687 2.389
OriginTransplant:SiteSS 8.579 25.013 0.343
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.522
SiteSS -0.522 0.500
OrgnTrn:SSS 0.369 -0.707 -0.707eta_squared(Prot.C.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 8.22e-04 | [0.00, 1.00]
Site | 0.25 | [0.08, 1.00]
Origin:Site | 2.79e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Protein Symbiont
##Check normality
hist(Coral.TP2$TP_ug.cm2_S)
shapiro.test(Coral.TP2$TP_ug.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP2$TP_ug.cm2_S
W = 0.95982, p-value = 0.09929#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP2<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Prot.S.lme_res_TP2 <- simulateResiduals(fittedModel = Prot.S.lme_TP2, plot = F)
plot(Prot.S.lme_res_TP2)
##Model results
summary(Prot.S.lme_TP2)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: 493.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.5114 -0.6809 -0.1247 0.6145 2.6570
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 796.1 28.22
Residual 3198.1 56.55
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 329.07 23.06 14.269
OriginTransplant -29.47 23.09 -1.277
SiteSS 43.25 23.09 1.873
OriginTransplant:SiteSS 35.17 32.65 1.077
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.501
SiteSS -0.501 0.500
OrgnTrn:SSS 0.354 -0.707 -0.707eta_squared(Prot.S.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.01 | [0.00, 1.00]
Site | 0.25 | [0.08, 1.00]
Origin:Site | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Prot.S.lme_TP2_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Prot.S.lme_TP2_KL)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "KL"), ]
REML criterion at convergence: 238.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.4406 -0.8006 0.2111 0.5424 1.7322
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 688.7 26.24
Residual 2134.1 46.20
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 329.07 20.18 16.303
OriginTransplant -29.47 18.86 -1.563
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.467eta_squared(Prot.S.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.11 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Prot.S.lme_TP2_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Prot.S.lme_TP2_SS)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "SS"), ]
REML criterion at convergence: 251.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.3034 -0.8616 0.0128 0.6244 2.1515
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 1484 38.53
Residual 3840 61.96
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 372.313 28.545 13.043
OriginTransplant 5.698 25.297 0.225
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.443eta_squared(Prot.S.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 2.53e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP2_ProtSym.sum<-summarySE(Coral.TP2, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Protein across Treatments
TP2_ProtSym.plot<-ggplot(TP2_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(250, 450); TP2_ProtSym.plot
Biomass Host
##Check normality
hist(Coral.TP2$AFDW_mg.cm2_C)
shapiro.test(Coral.TP2$AFDW_mg.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP2$AFDW_mg.cm2_C
W = 0.98545, p-value = 0.8093#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP2<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Bio.C.lme_res_TP2 <- simulateResiduals(fittedModel = Bio.C.lme_TP2, plot = F)
plot(Bio.C.lme_res_TP2)
##Model results
summary(Bio.C.lme_TP2)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: -19.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.2645 -0.4732 -0.0846 0.5858 2.5448
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002429 0.04928
Residual 0.028719 0.16947
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.16312 0.05659 20.552
OriginTransplant -0.10184 0.06918 -1.472
SiteSS -0.11111 0.06918 -1.606
OriginTransplant:SiteSS 0.26062 0.09784 2.664
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.611
SiteSS -0.611 0.500
OrgnTrn:SSS 0.432 -0.707 -0.707eta_squared(Bio.C.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 8.00e-03 | [0.00, 1.00]
Site | 3.65e-03 | [0.00, 1.00]
Origin:Site | 0.14 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Bio.C.lme_TP2_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Bio.C.lme_TP2_KL)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "KL"), ]
REML criterion at convergence: -14.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.7100 -0.5598 -0.2566 0.3597 2.9272
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.001355 0.03681
Residual 0.023645 0.15377
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.16312 0.04921 23.634
OriginTransplant -0.10184 0.06278 -1.622
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.638eta_squared(Bio.C.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.12 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.C.lme_TP2_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])boundary (singular) fit: see help('isSingular')summary(Bio.C.lme_TP2_SS)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "SS"), ]
REML criterion at convergence: -5.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.0676 -0.4964 0.1545 0.7280 1.2050
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.03634 0.1906
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.05201 0.05503 19.12
OriginTransplant 0.15878 0.07783 2.04
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.C.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.16 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP2_BioHost.sum<-summarySE(Coral.TP2, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Host Biomass across Treatments
TP2_BioHost.plot<-ggplot(TP2_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0, 2)+
annotate("text", x=2, y=1.35, label="-", size=levels.sz, fontface="bold"); TP2_BioHost.plot
Biomass Symbionts
##Check normality
hist(Coral.TP2$AFDW_mg.cm2_S)
shapiro.test(Coral.TP2$AFDW_mg.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP2$AFDW_mg.cm2_S
W = 0.98263, p-value = 0.6911#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP2<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Bio.S.lme_res_TP2 <- simulateResiduals(fittedModel = Bio.S.lme_TP2, plot = F)
plot(Bio.S.lme_res_TP2)
##Model results
summary(Bio.S.lme_TP2)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: -50.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.89274 -0.61267 -0.05248 0.62439 1.64962
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0003874 0.01968
Residual 0.0146825 0.12117
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.71562 0.03678 19.457
OriginTransplant -0.12045 0.04947 -2.435
SiteSS 0.12854 0.04947 2.599
OriginTransplant:SiteSS 0.11020 0.06996 1.575
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.673
SiteSS -0.673 0.500
OrgnTrn:SSS 0.476 -0.707 -0.707eta_squared(Bio.S.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.08 | [0.00, 1.00]
Site | 0.40 | [0.21, 1.00]
Origin:Site | 0.06 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Bio.S.lme_TP2_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])boundary (singular) fit: see help('isSingular')summary(Bio.S.lme_TP2_KL)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "KL"), ]
REML criterion at convergence: -25.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.7872 -0.5807 -0.0580 0.6095 1.5698
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.01486 0.1219
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.71562 0.03519 20.34
OriginTransplant -0.12045 0.04977 -2.42
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.S.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.21 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.S.lme_TP2_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Bio.S.lme_TP2_SS)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "SS"), ]
REML criterion at convergence: -27.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.7098 -0.5388 -0.1840 0.6014 1.8524
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.004978 0.07056
Residual 0.011447 0.10699
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.84416 0.05112 16.513
OriginTransplant -0.01025 0.04368 -0.235
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.427eta_squared(Bio.S.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 2.74e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP2_BioSym.sum<-summarySE(Coral.TP2, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Biomass across Treatments
TP2_BioSym.plot<-ggplot(TP2_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0, 1)+
annotate("text", x=1, y=0.85, label="*", size=sig.sz, fontface="bold"); TP2_BioSym.plot
Chlorophyll
##Check normality
hist(Coral.TP2$Chl_ug.cm2)
shapiro.test(Coral.TP2$Chl_ug.cm2)
Shapiro-Wilk normality test
data: Coral.TP2$Chl_ug.cm2
W = 0.89322, p-value = 0.0003826#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP2<-lmer(Chl_ug.cm2~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Chl.lme_res_TP2 <- simulateResiduals(fittedModel = Chl.lme_TP2, plot = F)
plot(Chl.lme_res_TP2)
##Model results
summary(Chl.lme_TP2)Linear mixed model fit by REML ['lmerMod']
Formula: Chl_ug.cm2 ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: 60.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.5646 -0.6834 -0.2260 0.6331 2.9227
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.02999 0.1732
Residual 0.17246 0.4153
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.2824 0.1561 8.216
OriginTransplant -0.2475 0.1695 -1.460
SiteSS 0.5567 0.1695 3.284
OriginTransplant:SiteSS 0.6593 0.2398 2.750
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.543
SiteSS -0.543 0.500
OrgnTrn:SSS 0.384 -0.707 -0.707eta_squared(Chl.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.01 | [0.00, 1.00]
Site | 0.57 | [0.40, 1.00]
Origin:Site | 0.15 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Chl.lme_TP2_KL<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Chl.lme_TP2_KL)Linear mixed model fit by REML ['lmerMod']
Formula: Chl_ug.cm2 ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "KL"), ]
REML criterion at convergence: 4.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.4118 -0.7633 -0.1784 0.6717 2.0082
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.01088 0.1043
Residual 0.05353 0.2314
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.28244 0.08993 14.26
OriginTransplant -0.24748 0.09445 -2.62
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.525eta_squared(Chl.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.26 | [0.03, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Chl.lme_TP2_SS<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Chl.lme_TP2_SS)Linear mixed model fit by REML ['lmerMod']
Formula: Chl_ug.cm2 ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "SS"), ]
REML criterion at convergence: 36.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.2515 -0.7498 -0.1994 0.6152 2.0640
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.1653 0.4065
Residual 0.2069 0.4548
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.8391 0.2689 6.838
OriginTransplant 0.4118 0.1857 2.218
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.345eta_squared(Chl.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.20 | [0.01, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP2_Chl.sum<-summarySE(Coral.TP2, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Chlorophyll across Treatments
TP2_Chl.plot<-ggplot(TP2_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(0, 2.5)+
annotate("text", x=c(1,2), y=c(1.5, 2.5), label="*", size=sig.sz, fontface="bold"); TP2_Chl.plot
TP3
##Subset Timepoint 3
Coral.TP3<-subset(Coral.rm, TimeP=="TP3")
Protein Host
##Check normality
hist(Coral.TP3$TP_ug.cm2_C)
shapiro.test(Coral.TP3$TP_ug.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP3$TP_ug.cm2_C
W = 0.97395, p-value = 0.3582#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP3<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Prot.C.lme_res_TP3 <- simulateResiduals(fittedModel = Prot.C.lme_TP3, plot = F)
plot(Prot.C.lme_res_TP3)
##Model results
summary(Prot.C.lme_TP3)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: 507.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.52281 -0.56107 -0.09932 0.69343 2.85970
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 1765 42.01
Residual 4332 65.82
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 354.577 30.812 11.508
OriginTransplant -28.338 26.869 -1.055
SiteSS 11.510 26.869 0.428
OriginTransplant:SiteSS 6.695 37.999 0.176
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.436
SiteSS -0.436 0.500
OrgnTrn:SSS 0.308 -0.707 -0.707eta_squared(Prot.C.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.04 | [0.00, 1.00]
Site | 0.01 | [0.00, 1.00]
Origin:Site | 7.38e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Protein Symbiont
##Check normality
hist(Coral.TP3$TP_ug.cm2_S)
shapiro.test(Coral.TP3$TP_ug.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP3$TP_ug.cm2_S
W = 0.95992, p-value = 0.1002#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP3<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Prot.S.lme_res_TP3 <- simulateResiduals(fittedModel = Prot.S.lme_TP3, plot = F)
plot(Prot.S.lme_res_TP3)
##Model results
summary(Prot.S.lme_TP3)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: 497.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.04853 -0.63564 -0.04301 0.40723 2.41329
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 606.5 24.63
Residual 3588.9 59.91
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 350.04 22.39 15.635
OriginTransplant -31.07 24.46 -1.270
SiteSS 54.24 24.46 2.218
OriginTransplant:SiteSS 40.43 34.59 1.169
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.546
SiteSS -0.546 0.500
OrgnTrn:SSS 0.386 -0.707 -0.707eta_squared(Prot.S.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 9.30e-03 | [0.00, 1.00]
Site | 0.31 | [0.13, 1.00]
Origin:Site | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Prot.S.lme_TP3_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Prot.S.lme_TP3_KL)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "KL"), ]
REML criterion at convergence: 240.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.73973 -0.61677 -0.06905 0.34373 2.28780
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 267.5 16.36
Residual 2444.8 49.45
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 350.04 17.11 20.453
OriginTransplant -31.07 20.19 -1.539
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.590eta_squared(Prot.S.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.11 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Prot.S.lme_TP3_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Prot.S.lme_TP3_SS)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "SS"), ]
REML criterion at convergence: 255.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.80428 -0.53697 0.01298 0.48663 2.22260
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 939.7 30.66
Residual 4737.1 68.83
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 404.272 26.608 15.193
OriginTransplant 9.355 28.098 0.333
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.528eta_squared(Prot.S.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 5.51e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP3_ProtSym.sum<-summarySE(Coral.TP3, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Protein across Treatments
TP3_ProtSym.plot<-ggplot(TP3_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(250, 450); TP3_ProtSym.plot
Biomass Host
##Check normality
hist(Coral.TP3$AFDW_mg.cm2_C)
shapiro.test(Coral.TP3$AFDW_mg.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP3$AFDW_mg.cm2_C
W = 0.98042, p-value = 0.5962#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP3<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Bio.C.lme_res_TP3 <- simulateResiduals(fittedModel = Bio.C.lme_TP3, plot = F)
plot(Bio.C.lme_res_TP3)
##Model results
summary(Bio.C.lme_TP3)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: 1.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.39331 -0.54299 0.06699 0.61611 2.15929
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.007168 0.08467
Residual 0.045972 0.21441
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.18826 0.07887 15.066
OriginTransplant -0.12291 0.08753 -1.404
SiteSS 0.09539 0.08753 1.090
OriginTransplant:SiteSS 0.12089 0.12379 0.977
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.555
SiteSS -0.555 0.500
OrgnTrn:SSS 0.392 -0.707 -0.707eta_squared(Bio.C.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.02 | [0.00, 1.00]
Site | 0.13 | [0.01, 1.00]
Origin:Site | 0.02 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Bio.C.lme_TP3_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Bio.C.lme_TP3_KL)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "KL"), ]
REML criterion at convergence: 4.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.11835 -0.65919 -0.03638 0.66514 2.01105
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002671 0.05168
Residual 0.055878 0.23638
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.18826 0.07448 15.955
OriginTransplant -0.12291 0.09650 -1.274
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.648eta_squared(Bio.C.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.08 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.C.lme_TP3_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Bio.C.lme_TP3_SS)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "SS"), ]
REML criterion at convergence: -1.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.92899 -0.46522 0.05763 0.57468 2.06374
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.006692 0.0818
Residual 0.039684 0.1992
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.283651 0.074415 17.250
OriginTransplant -0.002024 0.081326 -0.025
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.546eta_squared(Bio.C.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 3.10e-05 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP3_BioHost.sum<-summarySE(Coral.TP3, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Host Biomass across Treatments
TP3_BioHost.plot<-ggplot(TP3_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0, 2); TP3_BioHost.plot
Biomass Symbionts
##Check normality
hist(Coral.TP3$AFDW_mg.cm2_S)
shapiro.test(Coral.TP3$AFDW_mg.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP3$AFDW_mg.cm2_S
W = 0.95017, p-value = 0.04062#Slightly non Normal
hist(log(Coral.TP3$AFDW_mg.cm2_S+1))
shapiro.test(log(Coral.TP3$AFDW_mg.cm2_S+1))
Shapiro-Wilk normality test
data: log(Coral.TP3$AFDW_mg.cm2_S + 1)
W = 0.97082, p-value = 0.2726#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP3<-lmer(log(AFDW_mg.cm2_S+1)~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Bio.S.lme_res_TP3 <- simulateResiduals(fittedModel = Bio.S.lme_TP3, plot = F)
plot(Bio.S.lme_res_TP3)
##Model results
summary(Bio.S.lme_TP3)Linear mixed model fit by REML ['lmerMod']
Formula: log(AFDW_mg.cm2_S + 1) ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: -59.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.9013 -0.6371 -0.1087 0.6759 2.0985
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.001717 0.04143
Residual 0.011351 0.10654
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.50341 0.03896 12.920
OriginTransplant -0.02185 0.04349 -0.502
SiteSS 0.07684 0.04349 1.767
OriginTransplant:SiteSS 0.04053 0.06151 0.659
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.558
SiteSS -0.558 0.500
OrgnTrn:SSS 0.395 -0.707 -0.707eta_squared(Bio.S.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 6.34e-05 | [0.00, 1.00]
Site | 0.19 | [0.04, 1.00]
Origin:Site | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Bio.S.lme_TP3_KL<-lmer(log(AFDW_mg.cm2_S+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Bio.S.lme_TP3_KL)Linear mixed model fit by REML ['lmerMod']
Formula: log(AFDW_mg.cm2_S + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "KL"), ]
REML criterion at convergence: -32.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.19334 -0.63480 -0.09404 0.31252 2.01340
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.001809 0.04253
Residual 0.009689 0.09843
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.50341 0.03755 13.405
OriginTransplant -0.02185 0.04018 -0.544
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.535eta_squared(Bio.S.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.S.lme_TP3_SS<-lmer(log(AFDW_mg.cm2_S+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Bio.S.lme_TP3_SS)Linear mixed model fit by REML ['lmerMod']
Formula: log(AFDW_mg.cm2_S + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "SS"), ]
REML criterion at convergence: -26.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.70864 -0.59241 -0.00109 0.59598 2.08623
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0008163 0.02857
Residual 0.0136003 0.11662
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.58025 0.03749 15.478
OriginTransplant 0.01868 0.04761 0.392
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.635eta_squared(Bio.S.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 7.63e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP3_BioSym.sum<-summarySE(Coral.TP3, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Biomass across Treatments
TP3_BioSym.plot<-ggplot(TP3_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0, 1); TP3_BioSym.plot
Chlorophyll
##Check normality
hist(Coral.TP3$Chl_ug.cm2)
shapiro.test(Coral.TP3$Chl_ug.cm2)
Shapiro-Wilk normality test
data: Coral.TP3$Chl_ug.cm2
W = 0.92502, p-value = 0.004491#Not normal
hist(log(Coral.TP3$Chl_ug.cm2+1))
shapiro.test(log(Coral.TP3$Chl_ug.cm2+1))
Shapiro-Wilk normality test
data: log(Coral.TP3$Chl_ug.cm2 + 1)
W = 0.97477, p-value = 0.3837#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP3<-lmer(log(Chl_ug.cm2+1)~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Chl.lme_res_TP3 <- simulateResiduals(fittedModel = Chl.lme_TP3, plot = F)
plot(Chl.lme_res_TP3)
##Model results
summary(Chl.lme_TP3)Linear mixed model fit by REML ['lmerMod']
Formula: log(Chl_ug.cm2 + 1) ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: -35.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.25062 -0.55117 -0.02076 0.43420 2.75244
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.01951 0.1397
Residual 0.01831 0.1353
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.81996 0.08960 9.151
OriginTransplant -0.10476 0.05525 -1.896
SiteSS 0.18045 0.05525 3.266
OriginTransplant:SiteSS 0.13664 0.07813 1.749
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.308
SiteSS -0.308 0.500
OrgnTrn:SSS 0.218 -0.707 -0.707eta_squared(Chl.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.02 | [0.00, 1.00]
Site | 0.49 | [0.31, 1.00]
Origin:Site | 0.07 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Chl.lme_TP3_KL<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Chl.lme_TP3_KL)Linear mixed model fit by REML ['lmerMod']
Formula: log(Chl_ug.cm2 + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "KL"), ]
REML criterion at convergence: -19
Scaled residuals:
Min 1Q Median 3Q Max
-1.7968 -0.6704 -0.2815 0.8139 2.1670
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.009698 0.09848
Residual 0.016854 0.12982
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.8200 0.0681 12.041
OriginTransplant -0.1048 0.0530 -1.977
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.389eta_squared(Chl.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.16 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Chl.lme_TP3_SS<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Chl.lme_TP3_SS)Linear mixed model fit by REML ['lmerMod']
Formula: log(Chl_ug.cm2 + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "SS"), ]
REML criterion at convergence: -16.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.11066 -0.45318 -0.01187 0.63929 2.41481
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.03310 0.1819
Residual 0.01702 0.1305
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.00040 0.11159 8.965
OriginTransplant 0.03189 0.05326 0.599
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.239eta_squared(Chl.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.02 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP3_Chl.sum<-summarySE(Coral.TP3, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Chlorophyll across Treatments
TP3_Chl.plot<-ggplot(TP3_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(0, 2.5)+
annotate("text", x=1, y=1.7, label="-", size=levels.sz, fontface="bold"); TP3_Chl.plot
TP4
##Subset Timepoint 4
Coral.TP4<-subset(Coral.rm, TimeP=="TP4")
Protein Host
##Check normality
hist(Coral.TP4$TP_ug.cm2_C)
shapiro.test(Coral.TP4$TP_ug.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP4$TP_ug.cm2_C
W = 0.97592, p-value = 0.4503#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP4<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP4)
##Check residuals
Prot.C.lme_res_TP4 <- simulateResiduals(fittedModel = Prot.C.lme_TP4, plot = F)
plot(Prot.C.lme_res_TP4)
##Model results
summary(Prot.C.lme_TP4)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: 492.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.82648 -0.74846 0.02454 0.66400 1.93010
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 23.74 4.872
Residual 5739.32 75.758
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 329.20 22.05 14.930
OriginTransplant 21.29 31.63 0.673
SiteSS 50.44 30.93 1.631
OriginTransplant:SiteSS -67.31 44.72 -1.505
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.686
SiteSS -0.701 0.489
OrgnTrn:SSS 0.485 -0.707 -0.692eta_squared(Prot.C.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 7.51e-03 | [0.00, 1.00]
Site | 0.01 | [0.00, 1.00]
Origin:Site | 0.05 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Protein Symbiont
##Check normality
hist(Coral.TP4$TP_ug.cm2_S)
shapiro.test(Coral.TP4$TP_ug.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP4$TP_ug.cm2_S
W = 0.98874, p-value = 0.9316#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP4<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP4)
##Check residuals
Prot.S.lme_res_TP4 <- simulateResiduals(fittedModel = Prot.S.lme_TP4, plot = F)
plot(Prot.S.lme_res_TP4)
##Model results
summary(Prot.S.lme_TP4)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: 464
Scaled residuals:
Min 1Q Median 3Q Max
-1.9674 -0.6915 -0.1394 0.7468 1.8746
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 258.6 16.08
Residual 2793.9 52.86
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 283.146 17.862 15.852
OriginTransplant 2.715 22.078 0.123
SiteSS 94.191 21.579 4.365
OriginTransplant:SiteSS -14.298 31.203 -0.458
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.590
SiteSS -0.604 0.489
OrgnTrn:SSS 0.418 -0.707 -0.692eta_squared(Prot.S.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 2.00e-03 | [0.00, 1.00]
Site | 0.44 | [0.25, 1.00]
Origin:Site | 5.22e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Prot.S.lme_TP4_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Prot.S.lme_TP4_KL)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "KL"), ]
REML criterion at convergence: 233.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.9026 -0.5130 -0.1508 0.5988 1.9630
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 123.5 11.11
Residual 3004.0 54.81
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 283.146 17.074 16.584
OriginTransplant 2.801 22.890 0.122
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.641eta_squared(Prot.S.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 7.84e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Prot.S.lme_TP4_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Prot.S.lme_TP4_SS)Linear mixed model fit by REML ['lmerMod']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "SS"), ]
REML criterion at convergence: 231.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.95275 -0.71834 -0.03221 0.77696 1.62897
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 300.3 17.33
Residual 2653.9 51.52
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 377.34 17.92 21.05
OriginTransplant -10.98 21.53 -0.51
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.573eta_squared(Prot.S.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP4_ProtSym.sum<-summarySE(Coral.TP4, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Protein across Treatments
TP4_ProtSym.plot<-ggplot(TP4_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(250, 450); TP4_ProtSym.plot
Biomass Host
##Check normality
hist(Coral.TP4$AFDW_mg.cm2_C)
shapiro.test(Coral.TP4$AFDW_mg.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP4$AFDW_mg.cm2_C
W = 0.98656, p-value = 0.8669#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP4<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP4)
##Check residuals
Bio.C.lme_res_TP4 <- simulateResiduals(fittedModel = Bio.C.lme_TP4, plot = F)
plot(Bio.C.lme_res_TP4)
##Model results
summary(Bio.C.lme_TP4)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: 31.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.94819 -0.65333 0.01107 0.59003 2.21357
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0006586 0.02566
Residual 0.0974518 0.31217
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.99501 0.09133 10.895
OriginTransplant 0.17260 0.13032 1.324
SiteSS 0.33916 0.12744 2.661
OriginTransplant:SiteSS -0.38438 0.18428 -2.086
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.682
SiteSS -0.698 0.489
OrgnTrn:SSS 0.483 -0.707 -0.692eta_squared(Bio.C.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 1.12e-03 | [0.00, 1.00]
Site | 0.06 | [0.00, 1.00]
Origin:Site | 0.10 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Bio.C.lme_TP4_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])boundary (singular) fit: see help('isSingular')summary(Bio.C.lme_TP4_KL)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "KL"), ]
REML criterion at convergence: 22.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.65092 -0.56347 0.01929 0.47522 1.89563
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0000 0.0000
Residual 0.1361 0.3689
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.9950 0.1065 9.344
OriginTransplant 0.1727 0.1540 1.121
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.692
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.C.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.06 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.C.lme_TP4_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Bio.C.lme_TP4_SS)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "SS"), ]
REML criterion at convergence: 4.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.2767 -0.7007 0.1653 0.5506 2.0180
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.009792 0.09895
Residual 0.052524 0.22918
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.33417 0.08741 15.263
OriginTransplant -0.20931 0.09578 -2.185
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.523eta_squared(Bio.C.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.20 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP4_BioHost.sum<-summarySE(Coral.TP4, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Host Biomass across Treatments
TP4_BioHost.plot<-ggplot(TP4_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0, 2)+
annotate("text", x=2, y=1.6, label="*", size=sig.sz, fontface="bold"); TP4_BioHost.plot
Biomass Symbionts
##Check normality
hist(Coral.TP4$AFDW_mg.cm2_S)
shapiro.test(Coral.TP4$AFDW_mg.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP4$AFDW_mg.cm2_S
W = 0.96144, p-value = 0.1304#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP4<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP4)boundary (singular) fit: see help('isSingular')##Check residuals
Bio.S.lme_res_TP4 <- simulateResiduals(fittedModel = Bio.S.lme_TP4, plot = F)
plot(Bio.S.lme_res_TP4)
##Model results
summary(Bio.S.lme_TP4)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: -13.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.9850 -0.7648 -0.1116 0.6976 1.8287
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.03381 0.1839
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.58851 0.05308 11.087
OriginTransplant 0.02029 0.07675 0.264
SiteSS 0.15893 0.07507 2.117
OriginTransplant:SiteSS -0.05653 0.10855 -0.521
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.692
SiteSS -0.707 0.489
OrgnTrn:SSS 0.489 -0.707 -0.692
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.S.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 5.14e-04 | [0.00, 1.00]
Site | 0.12 | [0.01, 1.00]
Origin:Site | 6.42e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Bio.S.lme_TP4_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])boundary (singular) fit: see help('isSingular')summary(Bio.S.lme_TP4_KL)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "KL"), ]
REML criterion at convergence: -9
Scaled residuals:
Min 1Q Median 3Q Max
-1.8536 -0.7454 -0.2379 0.7005 1.9338
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.03023 0.1739
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.58851 0.05019 11.72
OriginTransplant 0.02029 0.07258 0.28
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.692
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.S.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 3.71e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.S.lme_TP4_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])boundary (singular) fit: see help('isSingular')summary(Bio.S.lme_TP4_SS)Linear mixed model fit by REML ['lmerMod']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "SS"), ]
REML criterion at convergence: -4.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.8877 -0.8214 0.1483 0.7055 1.5821
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.03739 0.1934
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.74744 0.05582 13.391
OriginTransplant -0.03625 0.08071 -0.449
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.692
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.S.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 9.51e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP4_BioSym.sum<-summarySE(Coral.TP4, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Biomass across Treatments
TP4_BioSym.plot<-ggplot(TP4_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0, 1); TP4_BioSym.plot
Chlorophyll
##Check normality
hist(Coral.TP4$Chl_ug.cm2)
shapiro.test(Coral.TP4$Chl_ug.cm2)
Shapiro-Wilk normality test
data: Coral.TP4$Chl_ug.cm2
W = 0.94799, p-value = 0.03927#Slightly non normal
hist(log(Coral.TP4$Chl_ug.cm2+1))
shapiro.test(log(Coral.TP4$Chl_ug.cm2+1))
Shapiro-Wilk normality test
data: log(Coral.TP4$Chl_ug.cm2 + 1)
W = 0.97457, p-value = 0.4047#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP4<-lmer(log(Chl_ug.cm2+1)~Origin*Site+(1|Genotype), data=Coral.TP4)
##Check residuals
Chl.lme_res_TP4 <- simulateResiduals(fittedModel = Chl.lme_TP4, plot = F)
plot(Chl.lme_res_TP4)
##Model results
summary(Chl.lme_TP4)Linear mixed model fit by REML ['lmerMod']
Formula: log(Chl_ug.cm2 + 1) ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: -35.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.5527 -0.6247 0.1083 0.5659 1.8596
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.02813 0.1677
Residual 0.01693 0.1301
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.939828 0.103867 9.048
OriginTransplant -0.005543 0.054370 -0.102
SiteSS 0.335684 0.053119 6.319
OriginTransplant:SiteSS 0.049977 0.076810 0.651
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.250
SiteSS -0.256 0.489
OrgnTrn:SSS 0.177 -0.706 -0.692eta_squared(Chl.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 6.34e-03 | [0.00, 1.00]
Site | 0.69 | [0.55, 1.00]
Origin:Site | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Chl.lme_TP4_KL<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Chl.lme_TP4_KL)Linear mixed model fit by REML ['lmerMod']
Formula: log(Chl_ug.cm2 + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "KL"), ]
REML criterion at convergence: -14
Scaled residuals:
Min 1Q Median 3Q Max
-1.59344 -0.58861 -0.05038 0.48137 1.88220
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.01194 0.1093
Residual 0.02028 0.1424
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.939828 0.075300 12.481
OriginTransplant -0.005243 0.059554 -0.088
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.377eta_squared(Chl.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 4.07e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Chl.lme_TP4_SS<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Chl.lme_TP4_SS)Linear mixed model fit by REML ['lmerMod']
Formula: log(Chl_ug.cm2 + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "SS"), ]
REML criterion at convergence: -25
Scaled residuals:
Min 1Q Median 3Q Max
-2.80744 -0.47238 0.03677 0.58308 1.31155
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.049098 0.22158
Residual 0.009964 0.09982
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.27551 0.13113 9.727
OriginTransplant 0.04346 0.04175 1.041
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.152eta_squared(Chl.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.05 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP4_Chl.sum<-summarySE(Coral.TP4, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Chlorophyll across Treatments
TP4_Chl.plot<-ggplot(TP4_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(0, 3.5); TP4_Chl.plot
Effect Size over Time
##Dataframe of effect size results
Phys.ES<-data.frame(TimeP=c(rep("TP1",8), rep("TP2",8), rep("TP3",8), rep("TP4",8)),
Site=c(rep(c("KL", "SS"),16)),
Metric=c(rep(c("AFDW_mg.cm2_C", "AFDW_mg.cm2_C", "AFDW_mg.cm2_S", "AFDW_mg.cm2_S", "TP_ug.cm2_S", "TP_ug.cm2_S", "Chl_ug.cm2", "Chl_ug.cm2"),4)),
EtaSq=c(eta_squared(Bio.C.lme_TP1_KL)$Eta2_partial,
eta_squared(Bio.C.lme_TP1_SS)$Eta2_partial,
eta_squared(Bio.S.lme_TP1_KL)$Eta2_partial,
eta_squared(Bio.S.lme_TP1_SS)$Eta2_partial,
eta_squared(Prot.S.lme_TP1_KL)$Eta2_partial,
eta_squared(Prot.S.lme_TP1_SS)$Eta2_partial,
eta_squared(Chl.lme_TP1_KL)$Eta2_partial,
eta_squared(Chl.lme_TP1_SS)$Eta2_partial,
eta_squared(Bio.C.lme_TP2_KL)$Eta2_partial,
eta_squared(Bio.C.lme_TP2_SS)$Eta2_partial,
eta_squared(Bio.S.lme_TP2_KL)$Eta2_partial,
eta_squared(Bio.S.lme_TP2_SS)$Eta2_partial,
eta_squared(Prot.S.lme_TP2_KL)$Eta2_partial,
eta_squared(Prot.S.lme_TP2_SS)$Eta2_partial,
eta_squared(Chl.lme_TP2_KL)$Eta2_partial,
eta_squared(Chl.lme_TP2_SS)$Eta2_partial,
eta_squared(Bio.C.lme_TP3_KL)$Eta2_partial,
eta_squared(Bio.C.lme_TP3_SS)$Eta2_partial,
eta_squared(Bio.S.lme_TP3_KL)$Eta2_partial,
eta_squared(Bio.S.lme_TP3_SS)$Eta2_partial,
eta_squared(Prot.S.lme_TP3_KL)$Eta2_partial,
eta_squared(Prot.S.lme_TP3_SS)$Eta2_partial,
eta_squared(Chl.lme_TP3_KL)$Eta2_partial,
eta_squared(Chl.lme_TP3_SS)$Eta2_partial,
eta_squared(Bio.C.lme_TP4_KL)$Eta2_partial,
eta_squared(Bio.C.lme_TP4_SS)$Eta2_partial,
eta_squared(Bio.S.lme_TP4_KL)$Eta2_partial,
eta_squared(Bio.S.lme_TP4_SS)$Eta2_partial,
eta_squared(Prot.S.lme_TP4_KL)$Eta2_partial,
eta_squared(Prot.S.lme_TP4_SS)$Eta2_partial,
eta_squared(Chl.lme_TP4_KL)$Eta2_partial,
eta_squared(Chl.lme_TP4_SS)$Eta2_partial),
Pvalue=c(summary(Bio.C.lme_TP1_KL)$coefficients[10],
summary(Bio.C.lme_TP1_SS)$coefficients[10],
summary(Bio.S.lme_TP1_KL)$coefficients[10],
summary(Bio.S.lme_TP1_SS)$coefficients[10],
summary(Prot.S.lme_TP1_KL)$coefficients[10],
summary(Prot.S.lme_TP1_SS)$coefficients[10],
summary(Chl.lme_TP1_KL)$coefficients[10],
summary(Chl.lme_TP1_SS)$coefficients[10],
summary(Bio.C.lme_TP2_KL)$coefficients[10],
summary(Bio.C.lme_TP2_SS)$coefficients[10],
summary(Bio.S.lme_TP2_KL)$coefficients[10],
summary(Bio.S.lme_TP2_SS)$coefficients[10],
summary(Prot.S.lme_TP2_KL)$coefficients[10],
summary(Prot.S.lme_TP2_SS)$coefficients[10],
summary(Chl.lme_TP2_KL)$coefficients[10],
summary(Chl.lme_TP2_SS)$coefficients[10],
summary(Bio.C.lme_TP3_KL)$coefficients[10],
summary(Bio.C.lme_TP3_SS)$coefficients[10],
summary(Bio.S.lme_TP3_KL)$coefficients[10],
summary(Bio.S.lme_TP3_SS)$coefficients[10],
summary(Prot.S.lme_TP3_KL)$coefficients[10],
summary(Prot.S.lme_TP3_SS)$coefficients[10],
summary(Chl.lme_TP3_KL)$coefficients[10],
summary(Chl.lme_TP3_SS)$coefficients[10],
summary(Bio.C.lme_TP4_KL)$coefficients[10],
summary(Bio.C.lme_TP4_SS)$coefficients[10],
summary(Bio.S.lme_TP4_KL)$coefficients[10],
summary(Bio.S.lme_TP4_SS)$coefficients[10],
summary(Prot.S.lme_TP4_KL)$coefficients[10],
summary(Prot.S.lme_TP4_SS)$coefficients[10],
summary(Chl.lme_TP4_KL)$coefficients[10],
summary(Chl.lme_TP4_SS)$coefficients[10]))
Phys.ES$Sig<-ifelse(Phys.ES$Pvalue<0.001, "***", ifelse(Phys.ES$Pvalue<0.01, "**", ifelse(Phys.ES$Pvalue<0.05, "*", ifelse(Phys.ES$Pvalue<0.1, "-", NA))))Host Biomass
Bio.C.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_C"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Host Biomass")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Bio.C.ES.plot
Symbiont Biomass
Bio.S.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Symbiont Biomass")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Bio.S.ES.plot
Symbiont Protein
Prot.S.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="TP_ug.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Symbiont Protein")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Prot.S.ES.plot
Chlorophyll
Chl.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="Chl_ug.cm2"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Chlorophyll")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Chl.ES.plot
Figures
Figure 2 Physiology Effect Size
Adjust Figures for Panel
##Host Biomass
Bio.C.ES.plot.cut<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_C"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Host Biomass")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"), legend.position=c(0.15, 0.8))+
labs(x="", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")
##Symbiont Biomass
Bio.S.ES.plot.cut<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Symbiont Biomass")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
labs(x="", y="", colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")
##Symbiont Protein
Prot.S.ES.plot.cut<-ggplot(Phys.ES[which(Phys.ES$Metric=="TP_ug.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Symbiont Protein")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")
##Chlorophyll
Chl.ES.plot.cut<-ggplot(Phys.ES[which(Phys.ES$Metric=="Chl_ug.cm2"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Chlorophyll")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
labs(x="Time Point", y="", colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")
Figure 2
##Create Panel
PhysES_fig<-plot_grid(Bio.C.ES.plot.cut, Bio.S.ES.plot.cut,
Prot.S.ES.plot.cut, Chl.ES.plot.cut,
rel_widths=c(0.9, 1, 0.9, 1),
rel_heights = c(1, 1, 1, 1),
nrow=2, ncol=2, byrow=T, labels = c("A", "B", "C", "D"))Warning: Removed 8 rows containing missing values (`geom_text()`).Warning: Removed 8 rows containing missing values (`geom_text()`).Warning: Removed 8 rows containing missing values (`geom_text()`).Warning: Removed 8 rows containing missing values (`geom_text()`).##Save Figure
ggsave(filename="Figures/02_Physiology/Fig2_PhysiologyEffectSizes.png", plot=PhysES_fig, dpi=300, width=10, height=8, units="in")
Figure S3 Univariate Panels
Adjust Figures for Panel
##Biomass Host
TP1_BioHost.plot.cut<-ggplot(TP1_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
ggtitle("TP1")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0.75, 1.5)+
annotate("text", x=1, y=1.35, label="*", size=levels.sz, fontface="bold")
TP2_BioHost.plot.cut<-ggplot(TP2_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
ggtitle("TP2")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(0.75, 1.5)+
annotate("text", x=2, y=1.35, label="-", size=levels.sz, fontface="bold")
TP3_BioHost.plot.cut<-ggplot(TP3_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
ggtitle("TP3")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(0.75, 1.5)
TP4_BioHost.plot.cut<-ggplot(TP4_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
ggtitle("TP4")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="", y="", colour="Origin")+
ylim(0.75, 1.5)+
annotate("text", x=2, y=1.45, label="*", size=levels.sz, fontface="bold")
##Biomass Symbiont
TP1_BioSym.plot.cut<-ggplot(TP1_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
ylim(0.45, 0.95)+
annotate("text", x=2, y=0.82, label="**", size=levels.sz, fontface="bold")
TP2_BioSym.plot.cut<-ggplot(TP2_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(0.45, 0.95)+
annotate("text", x=1, y=0.82, label="*", size=levels.sz, fontface="bold")
TP3_BioSym.plot.cut<-ggplot(TP3_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(0.45, 0.95)
TP4_BioSym.plot.cut<-ggplot(TP4_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="", y="", colour="Origin")+
ylim(0.45, 0.95)
##Protein Symbiont
TP1_ProtSym.plot.cut<-ggplot(TP1_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(250, 550)+
annotate("text", x=2, y=535, label="**", size=levels.sz, fontface="bold")
TP2_ProtSym.plot.cut<-ggplot(TP2_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(250, 550)
TP3_ProtSym.plot.cut<-ggplot(TP3_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(250, 550)
TP4_ProtSym.plot.cut<-ggplot(TP4_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="", y="", colour="Origin")+
ylim(250, 550)
##Chlorophyll
TP1_Chl.plot.cut<-ggplot(TP1_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="Site", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
ylim(0.5, 3.25)+
annotate("text", x=2, y=1.7, label="***", size=levels.sz, fontface="bold")
TP2_Chl.plot.cut<-ggplot(TP2_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="Site", y="", colour="Origin")+
ylim(0.5, 3.25)+
annotate("text", x=c(1,2), y=c(1.7, 2.6), label="*", size=levels.sz, fontface="bold")
TP3_Chl.plot.cut<-ggplot(TP3_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="Site", y="", colour="Origin")+
ylim(0.5, 3.25)+
annotate("text", x=1, y=1.7, label="-", size=levels.sz, fontface="bold")
TP4_Chl.plot.cut<-ggplot(TP4_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site", y="", colour="Origin")+
ylim(0.5, 3.25)Figure S3
##Create Panel
Phys_fig<-plot_grid(
TP1_BioHost.plot.cut,TP2_BioHost.plot.cut,TP3_BioHost.plot.cut,TP4_BioHost.plot.cut,
TP1_BioSym.plot.cut,TP2_BioSym.plot.cut,TP3_BioSym.plot.cut,TP4_BioSym.plot.cut,
TP1_ProtSym.plot.cut,TP2_ProtSym.plot.cut,TP3_ProtSym.plot.cut,TP4_ProtSym.plot.cut,
TP1_Chl.plot.cut,TP2_Chl.plot.cut,TP3_Chl.plot.cut,TP4_Chl.plot.cut,
rel_widths=c(rep(c(0.75, 0.75, 0.75,1),4)),
rel_heights = c(1,0.85,0.85,0.85),
nrow=4, ncol=4, byrow=T, labels = NULL)
##Save Figure
ggsave(filename="Figures/02_Physiology/FigS3_PhysiologyMetrics.png", plot=Phys_fig, dpi=300, width=12, height=14, units="in")
---
title: "Coral Physiology Following Reciprocal Transplant"
author: "Serena Hackerott"
date: "1/1/2024"
output:
  html_notebook:
    toc: yes
    toc_float: yes
  html_document:
    toc: yes
    df_print: paged
---

# Setup

```{r Setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```


### Load Packages
```{r}
##Install Packages if Needed
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("cowplot")) install.packages("cowplot")
if (!require("Rmisc")) install.packages("Rmisc")
if (!require("lme4")) install.packages("lme4")
if (!require("vegan")) install.packages("vegan")
if (!require("corrplot")) install.packages("corrplot")
if (!require("DHARMa")) install.packages("DHARMa")
if (!require("effectsize")) install.packages("effectsize")
if (!require("emmeans")) install.packages("emmeans")

##Load Packages
library(ggplot2) #Required for ggplots
library(cowplot) #Required for plotting panel figures
library(Rmisc) #Required for SummarySE function
library(lme4) #Required for mixed effects modeling
library(vegan) #Required for multivariate analysis PERM
library(corrplot) #Required for correlation plot
library(DHARMa) #Required to check residuals of mixed effects models
library(effectsize) #Required for eta_squared effect sizes
library(emmeans) #Required for pairwise comparisons 

```


### Graphing Parameters
```{r}
#Note: Run "Graphing Parameters" section from 01_ExperimentalSetup.Rmd file
```


# Sample Data and Metadata

### Load and Organize Data
```{r}
##Load Data
#Note: Physiological metrics calculated in 02_PhysiologyMetrics.R file
Coral<-read.csv("Outputs/CoralData.csv", header=TRUE)

##Set factor variables
Coral$TimeP<-factor(Coral$TimeP, levels=c("TP1", "TP2", "TP3", "TP4"))
Coral$Site<-factor(Coral$Site, levels=c("KL", "SS"))
Coral$Genotype<-factor(Coral$Genotype, levels=c("AC8", "AC10", "AC12"))
Coral$Orig<-factor(Coral$Orig, levels=c("N", "T"))
Coral$Origin<-factor(Coral$Origin, levels=c("Native", "Transplant"))
Coral$Site.Orig<-factor(Coral$Site.Orig, levels=c("KL.N", "KL.T", "SS.N", "SS.T"))


```


# Multivariate Analysis

### adonis OmegaSq Function
```{r}
#' Calculate (partial) Omega-squared (effect-size calculation) for PERMANOVA and add it to the input object
#'
#' @param adonisOutput An adonis object
#' @param partial Should partial omega-squared be calculated (sample size adjusted). Default TRUE
#' @return Original adonis object with the (partial) Omega-squared values added
#' @import vegan
#' @export
adonis_OmegaSq <- function(adonisOutput, partial = TRUE){
    if(!(is(adonisOutput, "adonis") || is(adonisOutput, "anova.cca")))
        stop("Input should be an adonis object")
    if (is(adonisOutput, "anova.cca")) {
        aov_tab <- adonisOutput
        aov_tab$MeanSqs <- aov_tab$SumOfSqs / aov_tab$Df
        aov_tab$MeanSqs[length(aov_tab$Df)] <- NA
    } else {
        aov_tab <- adonisOutput$aov.tab
    }
    heading <- attr(aov_tab, "heading")
    MS_res <- aov_tab[pmatch("Residual", rownames(aov_tab)), "MeanSqs"]
    SS_tot <- aov_tab[rownames(aov_tab) == "Total", "SumsOfSqs"]
    N <- aov_tab[rownames(aov_tab) == "Total", "Df"] + 1
    if(partial){
        omega <- apply(aov_tab, 1, function(x) (x["Df"]*(x["MeanSqs"]-MS_res))/(x["Df"]*x["MeanSqs"]+(N-x["Df"])*MS_res))
        aov_tab$parOmegaSq <- c(omega[1:(length(omega)-2)], NA, NA)
    } else {
        omega <- apply(aov_tab, 1, function(x) (x["SumsOfSqs"]-x["Df"]*MS_res)/(SS_tot+MS_res))
        aov_tab$OmegaSq <- c(omega[1:(length(omega)-2)], NA, NA)
    }
    if (is(adonisOutput, "adonis"))
        cn_order <- c("Df", "SumsOfSqs", "MeanSqs", "F.Model", "R2",
                      if (partial) "parOmegaSq" else "OmegaSq", "Pr(>F)")
    else
        cn_order <- c("Df", "SumOfSqs", "F", if (partial) "parOmegaSq" else "OmegaSq",
                      "Pr(>F)")
    aov_tab <- aov_tab[, cn_order]
    attr(aov_tab, "names") <- cn_order
    attr(aov_tab, "heading") <- heading
    if (is(adonisOutput, "adonis"))
        adonisOutput$aov.tab <- aov_tab
    else
        adonisOutput <- aov_tab
    return(adonisOutput)
}
```


### Check Correlation
```{r}
##Remove NA's
names(Coral)
Coral.rm<-na.omit(Coral)

##Log +1 transform 
Coral.log<-Coral.rm
Coral.log[,-c(1:10)]<-log(Coral.rm[,-c(1:10)]+1)
  
Phys.cor<-rcorr(as.matrix(Coral.log[,-c(1:10)]), type="pearson")
Phys.cor

diag(Phys.cor$P)<-0

corrplot(Phys.cor$r, type="upper", order="hclust", 
         p.mat = Phys.cor$P, sig.level = 0.01, insig = "blank")

```


### TP1
```{r}
##Subset Timepoint 1 
Coral.log_TP1<-subset(Coral.log, TimeP=="TP1")

```

#### PERMANOVA
```{r}
##PERMANOVA
Coral.TP1.perm<-adonis2(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin * Coral.log_TP1$Site, data=Coral.log_TP1, strata=Coral.log_TP1$Genotype, method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP1.perm)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin))

##Check dispersion by Site
anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Site))

##Check dispersion by Origin:Site
anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin:Coral.log_TP1$Site))

```
Physiology differs significantly by Origin and the effect of Origin differs between Sites.

#### Effect Size
Calculate Effect Size of Origin for each Site
```{r}
##KL
##PERMANOVA
Coral.TP1.perm_KL<-adonis2(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="KL")], data=Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),], strata=Coral.log_TP1$Genotype[which(Coral.log_TP1$Site=="KL")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP1.perm_KL)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="KL")]))


##SS
##PERMANOVA
Coral.TP1.perm_SS<-adonis2(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="SS")], data=Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),], strata=Coral.log_TP1$Genotype[which(Coral.log_TP1$Site=="SS")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP1.perm_SS)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="SS")]))

```


#### Variance Partitioning
```{r}
##Variance Partitioning
Coral.TP1.vp<-varpart(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Origin, Coral.log_TP1$Site, Coral.log_TP1$Genotype)
Coral.TP1.vp$part

##Check model significance
anova(rda(Coral.log_TP1[,c(11:14, 16)]~ Coral.log_TP1$Origin + Coral.log_TP1$Site + Coral.log_TP1$Genotype))

##Check variance explained by model
RsquareAdj(rda(Coral.log_TP1[,c(11:14, 16)]~ Coral.log_TP1$Origin + Coral.log_TP1$Site + Coral.log_TP1$Genotype))$adj.r.squared*100

##Check effect of Origin 
anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Origin))
anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Origin, Coral.log_TP1[,c(4:5)]))

##Check effect of Site 
anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Site))
anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Site, Coral.log_TP1[,c(5,7)]))

##Check effect of Genotype 
anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Genotype))
anova(rda(Coral.log_TP1[,c(11:14, 16)], Coral.log_TP1$Genotype, Coral.log_TP1[,c(4,7)]))


```
The model is significant (p = 0.001) and explains 35.14% of the variance of physiology. The effect of Origin (controlling for Site and Genotype) is significant (p = 0.013) and explains 3.92%. The effect of Site (controlling for Origin and Genotype) is significant (p = 0.001) and explains 5.73%. The effect of Genotype (controlling for Origin and Site) is significant (p = 0.001) and explains 27.27%. 


### TP2
```{r}
##Subset Timepoint 2 
Coral.log_TP2<-subset(Coral.log, TimeP=="TP2")

```

#### PERMANOVA
```{r}
##PERMANOVA
Coral.TP2.perm<-adonis2(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin * Coral.log_TP2$Site, data=Coral.log_TP2, strata=Coral.log_TP2$Genotype, method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP2.perm)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin))

##Check dispersion by Site
anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Site))

##Check dispersion by Origin:Site
anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin:Coral.log_TP2$Site))

```


#### Effect Size
Calculate Effect Size of Origin for each Site
```{r}
##KL
##PERMANOVA
Coral.TP2.perm_KL<-adonis2(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="KL")], data=Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),], strata=Coral.log_TP2$Genotype[which(Coral.log_TP2$Site=="KL")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP2.perm_KL)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="KL")]))


##SS
##PERMANOVA
Coral.TP2.perm_SS<-adonis2(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="SS")], data=Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),], strata=Coral.log_TP2$Genotype[which(Coral.log_TP2$Site=="SS")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP2.perm_SS)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="SS")]))

```


### TP3
```{r}
##Subset Timepoint 3 
Coral.log_TP3<-subset(Coral.log, TimeP=="TP3")

```

#### PERMANOVA
```{r}
##PERMANOVA
Coral.TP3.perm<-adonis2(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin * Coral.log_TP3$Site, data=Coral.log_TP3, strata=Coral.log_TP3$Genotype, method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP3.perm)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin))

##Check dispersion by Site
anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Site))

##Check dispersion by Origin:Site
anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin:Coral.log_TP3$Site))

```


#### Effect Size
Calculate Effect Size of Origin for each Site
```{r}
##KL
##PERMANOVA
Coral.TP3.perm_KL<-adonis2(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="KL")], data=Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),], strata=Coral.log_TP3$Genotype[which(Coral.log_TP3$Site=="KL")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP3.perm_KL)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="KL")]))


##SS
##PERMANOVA
Coral.TP3.perm_SS<-adonis2(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="SS")], data=Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),], strata=Coral.log_TP3$Genotype[which(Coral.log_TP3$Site=="SS")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP3.perm_SS)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="SS")]))


```


### TP4
```{r}
##Subset Timepoint 4 
Coral.log_TP4<-subset(Coral.log, TimeP=="TP4")

```

#### PERMANOVA
```{r}
##PERMANOVA
Coral.TP4.perm<-adonis2(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin * Coral.log_TP4$Site, data=Coral.log_TP4, strata=Coral.log_TP4$Genotype, method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP4.perm)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin))

##Check dispersion by Site
anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Site))

##Check dispersion by Origin:Site
anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin:Coral.log_TP4$Site))

```


#### Effect Size
Calculate Effect Size of Origin for each Site
```{r}
##KL
##PERMANOVA
Coral.TP4.perm_KL<-adonis2(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="KL")], data=Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),], strata=Coral.log_TP4$Genotype[which(Coral.log_TP4$Site=="KL")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP4.perm_KL)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="KL")]))


##SS
##PERMANOVA
Coral.TP4.perm_SS<-adonis2(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="SS")], data=Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),], strata=Coral.log_TP4$Genotype[which(Coral.log_TP4$Site=="SS")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP4.perm_SS)

##Check dispersion by Origin
anova(betadisper(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="SS")]))

```


# Univariate 

### TP1

```{r}
##Subset Timepoint 1 
Coral.TP1<-subset(Coral.rm, TimeP=="TP1")

```


#### Protein Host 
```{r}
##Check normality
hist(Coral.TP1$TP_ug.cm2_C)
shapiro.test(Coral.TP1$TP_ug.cm2_C)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP1<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Prot.C.lme_res_TP1 <- simulateResiduals(fittedModel = Prot.C.lme_TP1, plot = F)
plot(Prot.C.lme_res_TP1)

##Model results
summary(Prot.C.lme_TP1)

eta_squared(Prot.C.lme_TP1)

```


#### Protein Symbiont 
```{r}
##Check normality
hist(Coral.TP1$TP_ug.cm2_S)
shapiro.test(Coral.TP1$TP_ug.cm2_S)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP1<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Prot.S.lme_res_TP1 <- simulateResiduals(fittedModel = Prot.S.lme_TP1, plot = F)
plot(Prot.S.lme_res_TP1)

##Model results
summary(Prot.S.lme_TP1)

eta_squared(Prot.S.lme_TP1)

```

Effect size of Origin for each Site
```{r}
##KL
Prot.S.lme_TP1_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Prot.S.lme_TP1_KL)
eta_squared(Prot.S.lme_TP1_KL)


##SS
Prot.S.lme_TP1_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Prot.S.lme_TP1_SS)
eta_squared(Prot.S.lme_TP1_SS)

```



```{r Plot Protein Symbiont TP1}
##Summary statistics by Site and Origin
TP1_ProtSym.sum<-summarySE(Coral.TP1, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Protein across Treatments
TP1_ProtSym.plot<-ggplot(TP1_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(350, 550)+
  annotate("text", x=2, y=535, label="**", size=sig.sz, fontface="bold"); TP1_ProtSym.plot
```


#### Biomass Host 
```{r}
##Check normality
hist(Coral.TP1$AFDW_mg.cm2_C)
shapiro.test(Coral.TP1$AFDW_mg.cm2_C)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP1<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Bio.C.lme_res_TP1 <- simulateResiduals(fittedModel = Bio.C.lme_TP1, plot = F)
plot(Bio.C.lme_res_TP1)

##Model results
summary(Bio.C.lme_TP1)

eta_squared(Bio.C.lme_TP1)

```


Effect size of Origin for each Site
```{r}
##KL
Bio.C.lme_TP1_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Bio.C.lme_TP1_KL)
eta_squared(Bio.C.lme_TP1_KL)

##SS
Bio.C.lme_TP1_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Bio.C.lme_TP1_SS)
eta_squared(Bio.C.lme_TP1_SS)

```


```{r Plot Biomass Host TP1}
##Summary statistics by Site and Origin
TP1_BioHost.sum<-summarySE(Coral.TP1, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Host Biomass across Treatments
TP1_BioHost.plot<-ggplot(TP1_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0, 2)+
   annotate("text", x=1, y=1.5, label="*", size=sig.sz, fontface="bold"); TP1_BioHost.plot
```



#### Biomass Symbionts 
```{r}
##Check normality
hist(Coral.TP1$AFDW_mg.cm2_S)
shapiro.test(Coral.TP1$AFDW_mg.cm2_S)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP1<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Bio.S.lme_res_TP1 <- simulateResiduals(fittedModel = Bio.S.lme_TP1, plot = F)
plot(Bio.S.lme_res_TP1)

##Model results
summary(Bio.S.lme_TP1)

eta_squared(Bio.S.lme_TP1)

```


Effect size of Origin for each Site
```{r}
##KL
Bio.S.lme_TP1_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Bio.S.lme_TP1_KL)
eta_squared(Bio.S.lme_TP1_KL)


##SS
Bio.S.lme_TP1_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Bio.S.lme_TP1_SS)
eta_squared(Bio.S.lme_TP1_SS)

```


```{r Plot Biomass Symbiont TP1}
##Summary statistics by Site and Origin
TP1_BioSym.sum<-summarySE(Coral.TP1, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Biomass across Treatments
TP1_BioSym.plot<-ggplot(TP1_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0, 1)+
  annotate("text", x=2, y=0.85, label="**", size=sig.sz, fontface="bold"); TP1_BioSym.plot
```


#### Chlorophyll
```{r}
##Check normality
hist(Coral.TP1$Chl_ug.cm2)
shapiro.test(Coral.TP1$Chl_ug.cm2)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP1<-lmer(Chl_ug.cm2~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Chl.lme_res_TP1 <- simulateResiduals(fittedModel = Chl.lme_TP1, plot = F)
plot(Chl.lme_res_TP1)

##Model results
summary(Chl.lme_TP1)

eta_squared(Chl.lme_TP1)

```


Effect size of Origin for each Site
```{r}
##KL
Chl.lme_TP1_KL<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Chl.lme_TP1_KL)
eta_squared(Chl.lme_TP1_KL)

##SS
Chl.lme_TP1_SS<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Chl.lme_TP1_SS)
eta_squared(Chl.lme_TP1_SS)

```


```{r Plot Chlorophyll TP1}
##Summary statistics by Site and Origin
TP1_Chl.sum<-summarySE(Coral.TP1, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Chlorophyll across Treatments
TP1_Chl.plot<-ggplot(TP1_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(0, 2)+
  annotate("text", x=2, y=1.7, label="***", size=sig.sz, fontface="bold"); TP1_Chl.plot
```


### TP2

```{r}
##Subset Timepoint 2 
Coral.TP2<-subset(Coral.rm, TimeP=="TP2")

```


#### Protein Host 
```{r}
##Check normality
hist(Coral.TP2$TP_ug.cm2_C)
shapiro.test(Coral.TP2$TP_ug.cm2_C)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP2<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Prot.C.lme_res_TP2 <- simulateResiduals(fittedModel = Prot.C.lme_TP2, plot = F)
plot(Prot.C.lme_res_TP2)

##Model results
summary(Prot.C.lme_TP2)

eta_squared(Prot.C.lme_TP2)

```


#### Protein Symbiont 
```{r}
##Check normality
hist(Coral.TP2$TP_ug.cm2_S)
shapiro.test(Coral.TP2$TP_ug.cm2_S)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP2<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Prot.S.lme_res_TP2 <- simulateResiduals(fittedModel = Prot.S.lme_TP2, plot = F)
plot(Prot.S.lme_res_TP2)

##Model results
summary(Prot.S.lme_TP2)

eta_squared(Prot.S.lme_TP2)


```

Effect size of Origin for each Site
```{r}
##KL
Prot.S.lme_TP2_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Prot.S.lme_TP2_KL)
eta_squared(Prot.S.lme_TP2_KL)

##SS
Prot.S.lme_TP2_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Prot.S.lme_TP2_SS)
eta_squared(Prot.S.lme_TP2_SS)

```



```{r Plot Protein Symbiont TP2}
##Summary statistics by Site and Origin
TP2_ProtSym.sum<-summarySE(Coral.TP2, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Protein across Treatments
TP2_ProtSym.plot<-ggplot(TP2_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(250, 450); TP2_ProtSym.plot
```


#### Biomass Host 
```{r}
##Check normality
hist(Coral.TP2$AFDW_mg.cm2_C)
shapiro.test(Coral.TP2$AFDW_mg.cm2_C)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP2<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Bio.C.lme_res_TP2 <- simulateResiduals(fittedModel = Bio.C.lme_TP2, plot = F)
plot(Bio.C.lme_res_TP2)

##Model results
summary(Bio.C.lme_TP2)

eta_squared(Bio.C.lme_TP2)

```


Effect size of Origin for each Site
```{r}
##KL
Bio.C.lme_TP2_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Bio.C.lme_TP2_KL)
eta_squared(Bio.C.lme_TP2_KL)

##SS
Bio.C.lme_TP2_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Bio.C.lme_TP2_SS)
eta_squared(Bio.C.lme_TP2_SS)

```


```{r Plot Biomass Host TP2}
##Summary statistics by Site and Origin
TP2_BioHost.sum<-summarySE(Coral.TP2, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Host Biomass across Treatments
TP2_BioHost.plot<-ggplot(TP2_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0, 2)+
  annotate("text", x=2, y=1.35, label="-", size=levels.sz, fontface="bold"); TP2_BioHost.plot
```


#### Biomass Symbionts 
```{r}
##Check normality
hist(Coral.TP2$AFDW_mg.cm2_S)
shapiro.test(Coral.TP2$AFDW_mg.cm2_S)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP2<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Bio.S.lme_res_TP2 <- simulateResiduals(fittedModel = Bio.S.lme_TP2, plot = F)
plot(Bio.S.lme_res_TP2)

##Model results
summary(Bio.S.lme_TP2)

eta_squared(Bio.S.lme_TP2)

```


Effect size of Origin for each Site
```{r}
##KL
Bio.S.lme_TP2_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Bio.S.lme_TP2_KL)
eta_squared(Bio.S.lme_TP2_KL)

##SS
Bio.S.lme_TP2_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Bio.S.lme_TP2_SS)
eta_squared(Bio.S.lme_TP2_SS)

```


```{r Plot Biomass Symbiont TP2}
##Summary statistics by Site and Origin
TP2_BioSym.sum<-summarySE(Coral.TP2, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Biomass across Treatments
TP2_BioSym.plot<-ggplot(TP2_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0, 1)+
  annotate("text", x=1, y=0.85, label="*", size=sig.sz, fontface="bold"); TP2_BioSym.plot
```


#### Chlorophyll
```{r}
##Check normality
hist(Coral.TP2$Chl_ug.cm2)
shapiro.test(Coral.TP2$Chl_ug.cm2)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP2<-lmer(Chl_ug.cm2~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Chl.lme_res_TP2 <- simulateResiduals(fittedModel = Chl.lme_TP2, plot = F)
plot(Chl.lme_res_TP2)

##Model results
summary(Chl.lme_TP2)

eta_squared(Chl.lme_TP2)

```


Effect size of Origin for each Site
```{r}
##KL
Chl.lme_TP2_KL<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Chl.lme_TP2_KL)
eta_squared(Chl.lme_TP2_KL)


##SS
Chl.lme_TP2_SS<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Chl.lme_TP2_SS)
eta_squared(Chl.lme_TP2_SS)

```


```{r Plot Chlorophyll TP2}
##Summary statistics by Site and Origin
TP2_Chl.sum<-summarySE(Coral.TP2, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Chlorophyll across Treatments
TP2_Chl.plot<-ggplot(TP2_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(0, 2.5)+
  annotate("text", x=c(1,2), y=c(1.5, 2.5), label="*", size=sig.sz, fontface="bold"); TP2_Chl.plot
```


### TP3

```{r}
##Subset Timepoint 3 
Coral.TP3<-subset(Coral.rm, TimeP=="TP3")

```


#### Protein Host 
```{r}
##Check normality
hist(Coral.TP3$TP_ug.cm2_C)
shapiro.test(Coral.TP3$TP_ug.cm2_C)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP3<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Prot.C.lme_res_TP3 <- simulateResiduals(fittedModel = Prot.C.lme_TP3, plot = F)
plot(Prot.C.lme_res_TP3)

##Model results
summary(Prot.C.lme_TP3)

eta_squared(Prot.C.lme_TP3)

```


#### Protein Symbiont 
```{r}
##Check normality
hist(Coral.TP3$TP_ug.cm2_S)
shapiro.test(Coral.TP3$TP_ug.cm2_S)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP3<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Prot.S.lme_res_TP3 <- simulateResiduals(fittedModel = Prot.S.lme_TP3, plot = F)
plot(Prot.S.lme_res_TP3)

##Model results
summary(Prot.S.lme_TP3)

eta_squared(Prot.S.lme_TP3)

```

Effect size of Origin for each Site
```{r}
##KL
Prot.S.lme_TP3_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Prot.S.lme_TP3_KL)
eta_squared(Prot.S.lme_TP3_KL)


##SS
Prot.S.lme_TP3_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Prot.S.lme_TP3_SS)
eta_squared(Prot.S.lme_TP3_SS)

```



```{r Plot Protein Symbiont TP3}
##Summary statistics by Site and Origin
TP3_ProtSym.sum<-summarySE(Coral.TP3, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Protein across Treatments
TP3_ProtSym.plot<-ggplot(TP3_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(250, 450); TP3_ProtSym.plot
```


#### Biomass Host 
```{r}
##Check normality
hist(Coral.TP3$AFDW_mg.cm2_C)
shapiro.test(Coral.TP3$AFDW_mg.cm2_C)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP3<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Bio.C.lme_res_TP3 <- simulateResiduals(fittedModel = Bio.C.lme_TP3, plot = F)
plot(Bio.C.lme_res_TP3)

##Model results
summary(Bio.C.lme_TP3)

eta_squared(Bio.C.lme_TP3)

```


Effect size of Origin for each Site
```{r}
##KL
Bio.C.lme_TP3_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Bio.C.lme_TP3_KL)
eta_squared(Bio.C.lme_TP3_KL)

##SS
Bio.C.lme_TP3_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Bio.C.lme_TP3_SS)
eta_squared(Bio.C.lme_TP3_SS)

```


```{r Plot Biomass Host TP3}
##Summary statistics by Site and Origin
TP3_BioHost.sum<-summarySE(Coral.TP3, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Host Biomass across Treatments
TP3_BioHost.plot<-ggplot(TP3_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0, 2); TP3_BioHost.plot
```


#### Biomass Symbionts 
```{r}
##Check normality
hist(Coral.TP3$AFDW_mg.cm2_S)
shapiro.test(Coral.TP3$AFDW_mg.cm2_S)
#Slightly non Normal

hist(log(Coral.TP3$AFDW_mg.cm2_S+1))
shapiro.test(log(Coral.TP3$AFDW_mg.cm2_S+1))
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP3<-lmer(log(AFDW_mg.cm2_S+1)~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Bio.S.lme_res_TP3 <- simulateResiduals(fittedModel = Bio.S.lme_TP3, plot = F)
plot(Bio.S.lme_res_TP3)

##Model results
summary(Bio.S.lme_TP3)

eta_squared(Bio.S.lme_TP3)

```


Effect size of Origin for each Site
```{r}
##KL
Bio.S.lme_TP3_KL<-lmer(log(AFDW_mg.cm2_S+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Bio.S.lme_TP3_KL)
eta_squared(Bio.S.lme_TP3_KL)

##SS
Bio.S.lme_TP3_SS<-lmer(log(AFDW_mg.cm2_S+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Bio.S.lme_TP3_SS)
eta_squared(Bio.S.lme_TP3_SS)
```


```{r Plot Biomass Symbiont TP3}
##Summary statistics by Site and Origin
TP3_BioSym.sum<-summarySE(Coral.TP3, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Biomass across Treatments
TP3_BioSym.plot<-ggplot(TP3_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0, 1); TP3_BioSym.plot
```


#### Chlorophyll
```{r}
##Check normality
hist(Coral.TP3$Chl_ug.cm2)
shapiro.test(Coral.TP3$Chl_ug.cm2)
#Not normal

hist(log(Coral.TP3$Chl_ug.cm2+1))
shapiro.test(log(Coral.TP3$Chl_ug.cm2+1))
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP3<-lmer(log(Chl_ug.cm2+1)~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Chl.lme_res_TP3 <- simulateResiduals(fittedModel = Chl.lme_TP3, plot = F)
plot(Chl.lme_res_TP3)

##Model results
summary(Chl.lme_TP3)

eta_squared(Chl.lme_TP3)

```


Effect size of Origin for each Site
```{r}
##KL
Chl.lme_TP3_KL<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Chl.lme_TP3_KL)
eta_squared(Chl.lme_TP3_KL)


##SS
Chl.lme_TP3_SS<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Chl.lme_TP3_SS)
eta_squared(Chl.lme_TP3_SS)

```


```{r Plot Chlorophyll TP3}
##Summary statistics by Site and Origin
TP3_Chl.sum<-summarySE(Coral.TP3, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Chlorophyll across Treatments
TP3_Chl.plot<-ggplot(TP3_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(0, 2.5)+
  annotate("text", x=1, y=1.7, label="-", size=levels.sz, fontface="bold"); TP3_Chl.plot
```



### TP4

```{r}
##Subset Timepoint 4 
Coral.TP4<-subset(Coral.rm, TimeP=="TP4")

```


#### Protein Host 
```{r}
##Check normality
hist(Coral.TP4$TP_ug.cm2_C)
shapiro.test(Coral.TP4$TP_ug.cm2_C)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP4<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Prot.C.lme_res_TP4 <- simulateResiduals(fittedModel = Prot.C.lme_TP4, plot = F)
plot(Prot.C.lme_res_TP4)

##Model results
summary(Prot.C.lme_TP4)

eta_squared(Prot.C.lme_TP4)

```


#### Protein Symbiont 
```{r}
##Check normality
hist(Coral.TP4$TP_ug.cm2_S)
shapiro.test(Coral.TP4$TP_ug.cm2_S)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP4<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Prot.S.lme_res_TP4 <- simulateResiduals(fittedModel = Prot.S.lme_TP4, plot = F)
plot(Prot.S.lme_res_TP4)

##Model results
summary(Prot.S.lme_TP4)

eta_squared(Prot.S.lme_TP4)

```

Effect size of Origin for each Site
```{r}
##KL
Prot.S.lme_TP4_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Prot.S.lme_TP4_KL)
eta_squared(Prot.S.lme_TP4_KL)

##SS
Prot.S.lme_TP4_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Prot.S.lme_TP4_SS)
eta_squared(Prot.S.lme_TP4_SS)
```



```{r Plot Protein Symbiont TP4}
##Summary statistics by Site and Origin
TP4_ProtSym.sum<-summarySE(Coral.TP4, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Protein across Treatments
TP4_ProtSym.plot<-ggplot(TP4_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(250, 450); TP4_ProtSym.plot
```


#### Biomass Host 
```{r}
##Check normality
hist(Coral.TP4$AFDW_mg.cm2_C)
shapiro.test(Coral.TP4$AFDW_mg.cm2_C)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP4<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Bio.C.lme_res_TP4 <- simulateResiduals(fittedModel = Bio.C.lme_TP4, plot = F)
plot(Bio.C.lme_res_TP4)

##Model results
summary(Bio.C.lme_TP4)

eta_squared(Bio.C.lme_TP4)

```


Effect size of Origin for each Site
```{r}

##KL
Bio.C.lme_TP4_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Bio.C.lme_TP4_KL)
eta_squared(Bio.C.lme_TP4_KL)

##SS
Bio.C.lme_TP4_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Bio.C.lme_TP4_SS)
eta_squared(Bio.C.lme_TP4_SS)


```


```{r Plot Biomass Host TP4}
##Summary statistics by Site and Origin
TP4_BioHost.sum<-summarySE(Coral.TP4, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Host Biomass across Treatments
TP4_BioHost.plot<-ggplot(TP4_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0, 2)+
  annotate("text", x=2, y=1.6, label="*", size=sig.sz, fontface="bold"); TP4_BioHost.plot
```


#### Biomass Symbionts 
```{r}
##Check normality
hist(Coral.TP4$AFDW_mg.cm2_S)
shapiro.test(Coral.TP4$AFDW_mg.cm2_S)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP4<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Bio.S.lme_res_TP4 <- simulateResiduals(fittedModel = Bio.S.lme_TP4, plot = F)
plot(Bio.S.lme_res_TP4)

##Model results
summary(Bio.S.lme_TP4)

eta_squared(Bio.S.lme_TP4)

```


Effect size of Origin for each Site
```{r}
##KL
Bio.S.lme_TP4_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Bio.S.lme_TP4_KL)
eta_squared(Bio.S.lme_TP4_KL)

##SS
Bio.S.lme_TP4_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Bio.S.lme_TP4_SS)
eta_squared(Bio.S.lme_TP4_SS)

```


```{r Plot Biomass Symbiont TP4}
##Summary statistics by Site and Origin
TP4_BioSym.sum<-summarySE(Coral.TP4, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Biomass across Treatments
TP4_BioSym.plot<-ggplot(TP4_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0, 1); TP4_BioSym.plot
```


#### Chlorophyll
```{r}
##Check normality
hist(Coral.TP4$Chl_ug.cm2)
shapiro.test(Coral.TP4$Chl_ug.cm2)
#Slightly non normal

hist(log(Coral.TP4$Chl_ug.cm2+1))
shapiro.test(log(Coral.TP4$Chl_ug.cm2+1))
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP4<-lmer(log(Chl_ug.cm2+1)~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Chl.lme_res_TP4 <- simulateResiduals(fittedModel = Chl.lme_TP4, plot = F)
plot(Chl.lme_res_TP4)

##Model results
summary(Chl.lme_TP4)

eta_squared(Chl.lme_TP4)

```


Effect size of Origin for each Site
```{r}

##KL
Chl.lme_TP4_KL<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Chl.lme_TP4_KL)
eta_squared(Chl.lme_TP4_KL)


##SS
Chl.lme_TP4_SS<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Chl.lme_TP4_SS)
eta_squared(Chl.lme_TP4_SS)

```


```{r Plot Chlorophyll TP4}
##Summary statistics by Site and Origin
TP4_Chl.sum<-summarySE(Coral.TP4, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Chlorophyll across Treatments
TP4_Chl.plot<-ggplot(TP4_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(0, 3.5); TP4_Chl.plot
```


### Effect Size over Time

```{r}
##Dataframe of effect size results
Phys.ES<-data.frame(TimeP=c(rep("TP1",8), rep("TP2",8), rep("TP3",8), rep("TP4",8)), 
                    Site=c(rep(c("KL", "SS"),16)),
                    Metric=c(rep(c("AFDW_mg.cm2_C", "AFDW_mg.cm2_C", "AFDW_mg.cm2_S", "AFDW_mg.cm2_S", "TP_ug.cm2_S", "TP_ug.cm2_S", "Chl_ug.cm2", "Chl_ug.cm2"),4)),
                    EtaSq=c(eta_squared(Bio.C.lme_TP1_KL)$Eta2_partial,
                             eta_squared(Bio.C.lme_TP1_SS)$Eta2_partial,
                             eta_squared(Bio.S.lme_TP1_KL)$Eta2_partial,
                             eta_squared(Bio.S.lme_TP1_SS)$Eta2_partial,
                             eta_squared(Prot.S.lme_TP1_KL)$Eta2_partial,
                             eta_squared(Prot.S.lme_TP1_SS)$Eta2_partial, 
                             eta_squared(Chl.lme_TP1_KL)$Eta2_partial,
                             eta_squared(Chl.lme_TP1_SS)$Eta2_partial, 
                      eta_squared(Bio.C.lme_TP2_KL)$Eta2_partial,
                             eta_squared(Bio.C.lme_TP2_SS)$Eta2_partial,
                             eta_squared(Bio.S.lme_TP2_KL)$Eta2_partial,
                             eta_squared(Bio.S.lme_TP2_SS)$Eta2_partial,
                             eta_squared(Prot.S.lme_TP2_KL)$Eta2_partial,
                             eta_squared(Prot.S.lme_TP2_SS)$Eta2_partial, 
                             eta_squared(Chl.lme_TP2_KL)$Eta2_partial,
                             eta_squared(Chl.lme_TP2_SS)$Eta2_partial, 
                        eta_squared(Bio.C.lme_TP3_KL)$Eta2_partial,
                             eta_squared(Bio.C.lme_TP3_SS)$Eta2_partial,
                             eta_squared(Bio.S.lme_TP3_KL)$Eta2_partial,
                             eta_squared(Bio.S.lme_TP3_SS)$Eta2_partial,
                             eta_squared(Prot.S.lme_TP3_KL)$Eta2_partial,
                             eta_squared(Prot.S.lme_TP3_SS)$Eta2_partial, 
                             eta_squared(Chl.lme_TP3_KL)$Eta2_partial,
                             eta_squared(Chl.lme_TP3_SS)$Eta2_partial, 
                      eta_squared(Bio.C.lme_TP4_KL)$Eta2_partial,
                             eta_squared(Bio.C.lme_TP4_SS)$Eta2_partial,
                             eta_squared(Bio.S.lme_TP4_KL)$Eta2_partial,
                             eta_squared(Bio.S.lme_TP4_SS)$Eta2_partial,
                             eta_squared(Prot.S.lme_TP4_KL)$Eta2_partial,
                             eta_squared(Prot.S.lme_TP4_SS)$Eta2_partial, 
                             eta_squared(Chl.lme_TP4_KL)$Eta2_partial,
                             eta_squared(Chl.lme_TP4_SS)$Eta2_partial),
                    Pvalue=c(summary(Bio.C.lme_TP1_KL)$coefficients[10],
                             summary(Bio.C.lme_TP1_SS)$coefficients[10],
                             summary(Bio.S.lme_TP1_KL)$coefficients[10],
                             summary(Bio.S.lme_TP1_SS)$coefficients[10],
                             summary(Prot.S.lme_TP1_KL)$coefficients[10],
                             summary(Prot.S.lme_TP1_SS)$coefficients[10], 
                             summary(Chl.lme_TP1_KL)$coefficients[10],
                             summary(Chl.lme_TP1_SS)$coefficients[10], 
                      summary(Bio.C.lme_TP2_KL)$coefficients[10],
                             summary(Bio.C.lme_TP2_SS)$coefficients[10],
                             summary(Bio.S.lme_TP2_KL)$coefficients[10],
                             summary(Bio.S.lme_TP2_SS)$coefficients[10],
                             summary(Prot.S.lme_TP2_KL)$coefficients[10],
                             summary(Prot.S.lme_TP2_SS)$coefficients[10], 
                             summary(Chl.lme_TP2_KL)$coefficients[10],
                             summary(Chl.lme_TP2_SS)$coefficients[10], 
                        summary(Bio.C.lme_TP3_KL)$coefficients[10],
                             summary(Bio.C.lme_TP3_SS)$coefficients[10],
                             summary(Bio.S.lme_TP3_KL)$coefficients[10],
                             summary(Bio.S.lme_TP3_SS)$coefficients[10],
                             summary(Prot.S.lme_TP3_KL)$coefficients[10],
                             summary(Prot.S.lme_TP3_SS)$coefficients[10], 
                             summary(Chl.lme_TP3_KL)$coefficients[10],
                             summary(Chl.lme_TP3_SS)$coefficients[10], 
                      summary(Bio.C.lme_TP4_KL)$coefficients[10],
                             summary(Bio.C.lme_TP4_SS)$coefficients[10],
                             summary(Bio.S.lme_TP4_KL)$coefficients[10],
                             summary(Bio.S.lme_TP4_SS)$coefficients[10],
                             summary(Prot.S.lme_TP4_KL)$coefficients[10],
                             summary(Prot.S.lme_TP4_SS)$coefficients[10], 
                             summary(Chl.lme_TP4_KL)$coefficients[10],
                             summary(Chl.lme_TP4_SS)$coefficients[10]))



Phys.ES$Sig<-ifelse(Phys.ES$Pvalue<0.001, "***", ifelse(Phys.ES$Pvalue<0.01, "**", ifelse(Phys.ES$Pvalue<0.05, "*", ifelse(Phys.ES$Pvalue<0.1, "-", NA))))
```


#### Host Biomass
```{r Plot Effect Size Host Biomass}
Bio.C.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_C"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Host Biomass")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
   labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Bio.C.ES.plot

```

#### Symbiont Biomass
```{r Plot Effect Size Symbiont Biomass}
Bio.S.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Symbiont Biomass")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
   labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Bio.S.ES.plot

```


#### Symbiont Protein
```{r Plot Effect Size Symbiont Protein}
Prot.S.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="TP_ug.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Symbiont Protein")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
   labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Prot.S.ES.plot

```


#### Chlorophyll
```{r Plot Effect Size Chlorophyll}
Chl.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="Chl_ug.cm2"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Chlorophyll")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
   labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Chl.ES.plot

```


# Figures

### Figure 2 Physiology Effect Size

#### Adjust Figures for Panel
```{r}
##Host Biomass
Bio.C.ES.plot.cut<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_C"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Host Biomass")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), legend.position=c(0.15, 0.8))+
   labs(x="", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")

##Symbiont Biomass
Bio.S.ES.plot.cut<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Symbiont Biomass")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
   labs(x="", y="", colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")

##Symbiont Protein
Prot.S.ES.plot.cut<-ggplot(Phys.ES[which(Phys.ES$Metric=="TP_ug.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Symbiont Protein")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
   labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")

##Chlorophyll
Chl.ES.plot.cut<-ggplot(Phys.ES[which(Phys.ES$Metric=="Chl_ug.cm2"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Chlorophyll")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
   labs(x="Time Point", y="", colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")


```


#### Figure 2
```{r}
##Create Panel
PhysES_fig<-plot_grid(Bio.C.ES.plot.cut, Bio.S.ES.plot.cut, 
                      Prot.S.ES.plot.cut, Chl.ES.plot.cut,
                    rel_widths=c(0.9, 1, 0.9, 1), 
                    rel_heights = c(1, 1, 1, 1),
                    nrow=2, ncol=2, byrow=T, labels = c("A", "B", "C", "D"))

##Save Figure
ggsave(filename="Figures/02_Physiology/Fig2_PhysiologyEffectSizes.png", plot=PhysES_fig, dpi=300, width=10, height=8, units="in")

```


### Figure S3 Univariate Panels

#### Adjust Figures for Panel
```{r}

##Biomass Host
TP1_BioHost.plot.cut<-ggplot(TP1_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  ggtitle("TP1")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
    axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0.75, 1.5)+
  annotate("text", x=1, y=1.35, label="*", size=levels.sz, fontface="bold")


TP2_BioHost.plot.cut<-ggplot(TP2_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  ggtitle("TP2")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
        axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(0.75, 1.5)+
  annotate("text", x=2, y=1.35, label="-", size=levels.sz, fontface="bold")

TP3_BioHost.plot.cut<-ggplot(TP3_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  ggtitle("TP3")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
        axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(0.75, 1.5)

TP4_BioHost.plot.cut<-ggplot(TP4_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  ggtitle("TP4")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
        axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="", y="", colour="Origin")+
  ylim(0.75, 1.5)+
  annotate("text", x=2, y=1.45, label="*", size=levels.sz, fontface="bold")


##Biomass Symbiont
TP1_BioSym.plot.cut<-ggplot(TP1_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour="Origin")+
  ylim(0.45, 0.95)+
  annotate("text", x=2, y=0.82, label="**", size=levels.sz, fontface="bold")


TP2_BioSym.plot.cut<-ggplot(TP2_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(0.45, 0.95)+
  annotate("text", x=1, y=0.82, label="*", size=levels.sz, fontface="bold")


TP3_BioSym.plot.cut<-ggplot(TP3_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(0.45, 0.95)

TP4_BioSym.plot.cut<-ggplot(TP4_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="", y="", colour="Origin")+
  ylim(0.45, 0.95)


##Protein Symbiont
TP1_ProtSym.plot.cut<-ggplot(TP1_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(250, 550)+
  annotate("text", x=2, y=535, label="**", size=levels.sz, fontface="bold")


TP2_ProtSym.plot.cut<-ggplot(TP2_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(250, 550)

TP3_ProtSym.plot.cut<-ggplot(TP3_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(250, 550)

TP4_ProtSym.plot.cut<-ggplot(TP4_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="", y="", colour="Origin")+
  ylim(250, 550)


##Chlorophyll
TP1_Chl.plot.cut<-ggplot(TP1_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="Site", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Origin")+
  ylim(0.5, 3.25)+
  annotate("text", x=2, y=1.7, label="***", size=levels.sz, fontface="bold")


TP2_Chl.plot.cut<-ggplot(TP2_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="Site", y="", colour="Origin")+
  ylim(0.5, 3.25)+
  annotate("text", x=c(1,2), y=c(1.7, 2.6), label="*", size=levels.sz, fontface="bold")


TP3_Chl.plot.cut<-ggplot(TP3_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="Site", y="", colour="Origin")+
  ylim(0.5, 3.25)+
  annotate("text", x=1, y=1.7, label="-", size=levels.sz, fontface="bold")

TP4_Chl.plot.cut<-ggplot(TP4_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site", y="", colour="Origin")+
  ylim(0.5, 3.25)
```


#### Figure S3
```{r}
##Create Panel
Phys_fig<-plot_grid(
  TP1_BioHost.plot.cut,TP2_BioHost.plot.cut,TP3_BioHost.plot.cut,TP4_BioHost.plot.cut,
  TP1_BioSym.plot.cut,TP2_BioSym.plot.cut,TP3_BioSym.plot.cut,TP4_BioSym.plot.cut,
  TP1_ProtSym.plot.cut,TP2_ProtSym.plot.cut,TP3_ProtSym.plot.cut,TP4_ProtSym.plot.cut,
  TP1_Chl.plot.cut,TP2_Chl.plot.cut,TP3_Chl.plot.cut,TP4_Chl.plot.cut,
                    rel_widths=c(rep(c(0.75, 0.75, 0.75,1),4)), 
                    rel_heights = c(1,0.85,0.85,0.85),
                    nrow=4, ncol=4, byrow=T, labels = NULL)

##Save Figure
ggsave(filename="Figures/02_Physiology/FigS3_PhysiologyMetrics.png", plot=Phys_fig, dpi=300, width=12, height=14, units="in")

```

